From b3991b1a3f336e148393ec394062637c865a2509 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Elias=20Ervel=C3=A4?= <elias.m.ervela@utu.fi>
Date: Wed, 12 Jan 2022 15:29:03 +0000
Subject: [PATCH] Upload New File

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 Round_2_-_Regression.ipynb | 2827 ++++++++++++++++++++++++++++++++++++
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 create mode 100644 Round_2_-_Regression.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "f4ccc1e89dcc970805dc0d65a45f1455",
+     "grade": false,
+     "grade_id": "cell-0ef3a793cfc0e37b",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "## Round 2 - Regression\n",
+    "\n",
+    "\n",
+    "<img src=\"../../../coursedata/R2_Regression/SomePhoto.jpg\" alt=\"Drawing\" style=\"width: 300px\"/>\n",
+    "\n",
+    "In this round, we consider ML problems involving data points with real-valued labels $y$, which represent some quantity of interest. We often refer to such ML problems as **regression problems**. We will apply some basic ML methods to solve a simple regression problem. These methods aim at finding or learning a useful predictor function $h(\\mathbf{x})$. Such a function allows to predict the label $y$ of a data point based on the features $\\mathbf{x}$. A wide range of machine learning methods is obtained by combining different choices for the type of predictor functions (hypothesis space) and loss function (the quality measure used to rank predictors). These different combinations offer different tradeoffs between **computational complexity, robustness (against perturbation of data), and accuracy**.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "36d16c9d327331e6d3fcb44c1152ef36",
+     "grade": false,
+     "grade_id": "cell-4872a33f422acba9",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "## Learning Goals\n",
+    "\n",
+    "After completing this round, you should\n",
+    "\n",
+    "- know how to formulate \"real-world\" applications as a regression problem by identifying data points, their features, and labels  \n",
+    "- know how to represent features and labels of data points using matrices and vectors (as numpy arrays)\n",
+    "- be able to apply ready-made regression methods to learn a useful predictor function from labeled data \n",
+    "- evaluate the quality of linear regression methods using different loss function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "5b50bfa22482dcb5f898c9c542a72682",
+     "grade": false,
+     "grade_id": "cell-200ca65238fcf8cb",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "## Background Material \n",
+    "\n",
+    "* [Video Lecture](https://www.youtube.com/watch?v=kHwlB_j7Hkc) on regression by [Prof. Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng) \n",
+    "\n",
+    "* How to represent images via numpy arrays (matrices) explained here https://www.youtube.com/watch?v=xECXZ3tyONo\n",
+    "\n",
+    "* Additional information on the Python libraries used in this exercise can be found here:\n",
+    "\n",
+    " - [NumPy](http://cs231n.github.io/python-numpy-tutorial/)\n",
+    " - [matplotlib](https://matplotlib.org/tutorials/index.html#introductory) \n",
+    " - [Images and Numpy Arrays](https://matplotlib.org/tutorials/introductory/images.html)\n",
+    " - [Pandas](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html#min)\n",
+    " - [Slicing numpy arrays](https://www.pythoninformer.com/python-libraries/numpy/index-and-slice/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "dc31046f561a81f3ba51640f913047ef",
+     "grade": false,
+     "grade_id": "cell-d64aa667351e92ee",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "## The Problem \n",
+    "\n",
+    "Assume you have secured an internship at the city planning department of Helsinki. Your job is to analyze aerial photographs of the city area. Unfortunately, some of the aerial photographs have been damaged. The damaged images have a fraction of their pixels replaced by completely black pixels. To improve the image quality you can use machine learning! \n",
+    "\n",
+    "Let us model the problem of recovering the original image as a machine learning problem. The data points in this problem are individual pixels of an image. Each data point (pixel) is characterized by certain features. The quantity of interest (the label) of a pixel is the correct grayscale value. For uncorrupted pixels we know the label which is given by the grayscale value ($0...255$) of that pixel. \n",
+    "\n",
+    "For the corrupted pixels we do not know the true label (grayscale value). Therefore, we have to learn a predictor which reads in the features of a corrupted pixel and predicts (guesses) the label (grayscale) value of that pixel. To learn a reasonable predictor, we can try to compare its predictions for the uncorrupted pixels for which we know the correct label (grayscale value). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "19e4f356a3c11add210b8bed3cd90f5e",
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+     "grade_id": "cell-422ba0b968b596ba",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
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+    }
+   },
+   "source": [
+    "<a id='handsondata'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "    \n",
+    "### Demo. Loading the Corrupted Image\n",
+    "    \n",
+    "The code snippet below reads in the corrupted aerial photograph and stores the grayscale values of the pixels in the numpy array `Photo`. The corrupted pixels are completely black (grayscale value is zero). We consider the pixel with coordinates (m,n) as corrupted if the grayscale value `Photo[m,n]` is smaller than 1.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
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+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
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+    }
+   },
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the library \"cv2\" provides methods for loading image files into numpy arrays \n",
+    "import cv2\n",
+    "# the library \"pyplot\" provides functions for plotting data \n",
+    "import matplotlib.pyplot as plt      \n",
+    "# the library \"numpy\" provides functions for matrices and vectors \n",
+    "import numpy as np \n",
+    "# the library \"sklearn.linear_model\" provides functions for fitting linear predictors to data points \n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "# the library \"sklearn.metrics\" provides functions for evaluating different loss functions \n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "# filename of image file containing corrupted pixels \n",
+    "corrupted = '/coursedata/R2_Regression/SomePhotoCorrupted.bmp'\n",
+    "\n",
+    "# read in the corrupted aerial photograph as grayscale (second argument 0) and store it in variable `Photo`\n",
+    "Photo = cv2.imread(corrupted, 0)\n",
+    "# make sure that photograph is represented by 100 by 100 pixels \n",
+    "Photo = cv2.resize(Photo, (100, 100))\n",
+    "# create a figure with two plots side by side\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(20, 10)) # read more about how subplots work at: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html\n",
+    "# plot photograph (first subplot)\n",
+    "ax[0].imshow(Photo, cmap='gray')\n",
+    "\n",
+    "# find indices of numpy array corresponding to corrupted (black) pixels\n",
+    "error_idx = np.where(Photo<1)  # determine corrupted pixels (those whose grayscale = 0)\n",
+    "# vertical position (\"row index\") of corrupted pixels \n",
+    "rows_err = error_idx[0]\n",
+    "# horizontal position (\"column index\") of corrupted pixels \n",
+    "cols_err = error_idx[1]\n",
+    "# plot photograph (second subplot)\n",
+    "ax[1].imshow(Photo, cmap='gray')\n",
+    "# mark corrupted pixels with a blue dot \n",
+    "ax[1].scatter(cols_err, rows_err)\n",
+    "# display the image \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the library \"cv2\" provides methods for loading image files into numpy arrays \n",
+    "import cv2\n",
+    "# the library \"pyplot\" provides functions for plotting data \n",
+    "import matplotlib.pyplot as plt      \n",
+    "# the library \"numpy\" provides functions for matrices and vectors \n",
+    "import numpy as np \n",
+    "# the library \"sklearn.linear_model\" provides functions for fitting linear predictors to data points \n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "# the library \"sklearn.metrics\" provides functions for evaluating different loss functions \n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "# filename of image file containing corrupted pixels \n",
+    "corrupted = '/coursedata/R2_Regression/SomePhotoCorrupted.bmp'\n",
+    "\n",
+    "# read in the corrupted aerial photograph as grayscale (second argument 0) and store it in variable `Photo`\n",
+    "Photo = cv2.imread(corrupted, 0)\n",
+    "# make sure that photograph is represented by 100 by 100 pixels \n",
+    "Photo = cv2.resize(Photo, (100, 100))\n",
+    "# create a figure with two plots side by side\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(20, 10)) # read more about how subplots work at: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html\n",
+    "# plot photograph (first subplot)\n",
+    "ax[0].imshow(Photo, cmap='gray')\n",
+    "\n",
+    "# find indices of numpy array corresponding to corrupted (black) pixels\n",
+    "error_idx = np.where(Photo<1)  # determine corrupted pixels (those whose grayscale = 0)\n",
+    "\n",
+    "# vertical position (\"row index\") of corrupted pixels \n",
+    "rows_err = error_idx[0]\n",
+    "# horizontal position (\"column index\") of corrupted pixels \n",
+    "cols_err = error_idx[1]\n",
+    "\n",
+    "# plot photograph (second subplot)\n",
+    "ax[1].imshow(Photo, cmap='gray')\n",
+    "# mark corrupted pixels with a blue dot \n",
+    "ax[1].scatter(cols_err, rows_err)\n",
+    "# display the image \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
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+   "source": [
+    "## The Data\n",
+    "\n",
+    "Our goal is to learn an accurate predictor $h(\\mathbf{x})$ for the grayscale $y$ of a particular pixel in an aerial photograph. The prediction $h(\\mathbf{x})$ is based on several features (characteristics) $\\mathbf{x} = \\big(x_{1},\\ldots,x_{n}\\big)^{T}$ of the pixel. Some of these features are obtained from the grayscale values of nearby pixels. We expect that the grayscale values of nearby pixels are typically similar. \n",
+    "\n",
+    "To learn a predictor $h(\\mathbf{x})$, we use uncorrupted pixels (those which are not black) in the aerial photograph. Let us enumerate these uncorrupted pixels by $i=1,\\ldots,m$. The $i$th uncorrupted pixels is characterized by the features $\\mathbf{x}^{(i)} \\in \\mathbb{R}^{n}$ and the grayscale value $y^{(i)}$ of the pixel itself. We then use ML methods to find (or learn) a useful predictor $h(\\mathbf{x})$ which yields a small average prediction error $h(\\mathbf{x}^{(i)}) - y^{(i)}$ for $i=1,\\ldots,m$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
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+   "source": [
+    "<a id='handsondata'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "    \n",
+    "### Demo. Get the features\n",
+    "    \n",
+    "The following code snippet defines a function `X,y= GetFeaturesLabels(m,n)` which reads in data of the pixels and their greyscales. The input parameters are the number `m` of data points and the number `n` of features to be used for each data point. The function returns a feature matrix $\\mathbf{X}$ and label vector $\\mathbf{y}$. \n",
+    "\n",
+    "The features $\\mathbf{x}^{(i)}$ describing pixels are stored in the rows of the numpy array `X` (of shape (m,n)) and the corresponding greyscale values $y^{(i)}$ in the numpy array `y` (shape (m,1)). The two numpy arrays represent the feature matrix $\\mathbf{X} = \\begin{pmatrix} \\mathbf{x}^{(1)} & \\ldots & \\mathbf{x}^{(m)} \\end{pmatrix}^{T}$ and the label vector $\\mathbf{y} = \\big( y^{(1)}, \\ldots, y^{(m)} \\big)^{T}$. \n",
+    " \n",
+    "A key challenge in ML is to find out the most relevant features of data points that will adequately describe its properties. This challenge is the subject of the ML subfield [Feature engineering](https://en.wikipedia.org/wiki/Feature_engineering).\n",
+    "\n",
+    "In this round, we will use features obtained form the neighborhood of some pixel $i$. The first feature (denoted `x1` in the code) is the average (mean) grayscale value the pixel neighborhood (collection of pixels which surround the pixel in question). In partiuclar, we will use $3 \\times 3$ pixel neighborhood to calculate the mean grayscale value. The second feature (denoted `x2` in the code) is the [median](https://en.wikipedia.org/wiki/Median) grayscale value of $3 \\times 3$ pixel neighborhood. \n",
+    "\n",
+    "In principle we can use any quantity as a feature. In particular, we can use additional features which are obtained by a random nubmer generator. Later, we will check if these random features (which obviously do not contain any relevant information about the label which we want to predict) are useful for building linear predictor (see \"Student Task Varying Number of Features\"). \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
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+     "cell_type": "markdown",
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+     "locked": true,
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+   },
+   "source": [
+    "<img src=\"../../../coursedata/R2_Regression/GetFeatures.jpg\" alt=\"Drawing\" style=\"width: 900px\"/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
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+     "cell_type": "code",
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+   "outputs": [],
+   "source": [
+    "# Pandas provides functions for loading (storing) data from (to) files\n",
+    "import pandas as pd  \n",
+    "# the library \"cv2\" provides powerful methods for image processing and computer vision\n",
+    "import cv2 \n",
+    "# import functions for displaying and plotting data \n",
+    "from matplotlib import pyplot as plt \n",
+    "from IPython.display import display, HTML\n",
+    "# library \"numpy\" provides matrix (represented by numpy arrays) operations \n",
+    "import numpy as np   \n",
+    "# library \"random\" provides functions for generating random numbers\n",
+    "import random\n",
+    "\n",
+    "def GetFeaturesLabels(m, n):\n",
+    "    \n",
+    "    # m - number of data points (pixels)\n",
+    "    # n - number of features\n",
+    "    \n",
+    "    # filename of image file containing corrupted pixels\n",
+    "    corrupted = '/coursedata/R2_Regression/SomePhotoCorrupted.bmp'\n",
+    "    \n",
+    "    # read corrupted image as numpy array\n",
+    "    Photo = cv2.imread(corrupted, 0)\n",
+    "    # set image size (100 by 100 pixels)\n",
+    "    Photo = cv2.resize(Photo, (100, 100))\n",
+    "    \n",
+    "    # get image height and width \n",
+    "    imgheight = Photo.shape[0]\n",
+    "    imgwidth = Photo.shape[1]\n",
+    "\n",
+    "    # determine \"uncorroputed pixels\" by finding indices of those pixels with grayscale value larger than 0\n",
+    "    good_idx = np.where(Photo > 0)\n",
+    "\n",
+    "    # store the vertical coordinate (row index) of uncorroputed pixels in numpy array `rows`\n",
+    "    rows = good_idx[0] \n",
+    "    # store the horizontal coordinate (column index) of uncorroputed pixels in numpy array `cols` \n",
+    "    cols = good_idx[1]\n",
+    "    \n",
+    "    # set pads for defining pixel neighborhood and augmenting the image\n",
+    "    wp = 1\n",
+    "    hp = 1\n",
+    "\n",
+    "    # augment image with stripes such that we can also define neighborhoods of border pixels \n",
+    "    # the values of these pixels are zero\n",
+    "    tmp = np.vstack((np.zeros((wp, imgwidth)), Photo, np.zeros((wp, imgwidth))))\n",
+    "    augmented = np.hstack((np.zeros((2*wp + imgheight, hp)), tmp, np.zeros((2*wp + imgheight, hp))))\n",
+    "\n",
+    "    # initialize feature vectors `x1`, `x2`and label vector `y` as numpy arrays \n",
+    "    x1 = np.zeros((m,1))\n",
+    "    x2 = np.zeros((m,1))\n",
+    "    y = np.zeros((m,1))\n",
+    "    \n",
+    "    # calculate the mean and median gray scale value of a pixel neighborhood \n",
+    "    # here we define 3x3 pixel matrix surrounding a pixel as its neighborhood \n",
+    "    for iter_datapoint in range(m):\n",
+    "        row = rows[iter_datapoint] + wp # add wp to get the index of same data point in augmented Photo\n",
+    "        col = cols[iter_datapoint] + hp # add hp to get the index of same data point in augmented Photo\n",
+    "\n",
+    "        # get the true label (gray scale value) of a datapoint (pixel)\n",
+    "        y[iter_datapoint] = augmented[row, col]\n",
+    "\n",
+    "        # get values of pixel with its neighborhood (3x3 matrix) from image\n",
+    "        neighbors = np.copy(augmented[(row-wp):(row+wp+1), (col-hp):(col+hp+1)])\n",
+    "        # set value of a data point to 0 in order to exlude this value from calculation\n",
+    "        # for the 3x3 array the indices for this data point(center of the neighborhood) is [1,1]\n",
+    "        neighbors[1,1] = 0\n",
+    "        \n",
+    "        # calculate the feature of a data point (pixel) - the mean and median gray level of the neighborhoud\n",
+    "        # zero values are exluded from calculation\n",
+    "        x1[iter_datapoint] = np.mean(neighbors[neighbors != 0])   \n",
+    "        x2[iter_datapoint] = np.median(neighbors[neighbors != 0]) \n",
+    "        \n",
+    "    np.random.seed(30) # this is done so that every time that below np.random.randn is called, it produces the same output. \n",
+    "                       # this is needed for testing purposes\n",
+    "    # lets add some \"extra features\" here \n",
+    "    X = np.hstack((x1, x2, np.random.randn(n,m).T)) \n",
+    "    \n",
+    "    X = X[:,:n]\n",
+    "    return X, y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "249e94734fd8671cc2db97c6bc028933",
+     "grade": false,
+     "grade_id": "cell-b85dc13f39e40ddd",
+     "locked": true,
+     "schema_version": 3,
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+   },
+   "source": [
+    "## Visualize Data\n",
+    "\n",
+    "Scatter plots visualize data points by representing them as \"dots\" in the two-dimensional plane. Scatter plots can help to develop an intuition for the relation between features and labels of data points. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
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+   },
+   "source": [
+    "<a id='drawplot'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "    \n",
+    "### Demo. Scatterplots of Features and Labels\n",
+    "\n",
+    "<p>The code snippet below creates scatterplots showing the first two features $x^{(i)}_{1}$, $x^{(i)}_{2}$ and the label $y^{(i)}$ (greyscale) for each data point. It also creates a third scatterplot with the third feature $x^{(i)}_{3}$ and the label $y^{(i)}$ (greyscale) for each data point.</p>\n",
+    "</div>"
+   ]
+  },
+  {
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+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# pyplot library provides functions for plotting data \n",
+    "from matplotlib import pyplot as plt \n",
+    "\n",
+    "# read in features and labels of m=1000 data points, each of which \n",
+    "# is characterized by n=10 features \n",
+    "X,y = GetFeaturesLabels(1000, 10)\n",
+    "\n",
+    "# create a figure with 3 subplots arranged in 1 row \n",
+    "fig, axs = plt.subplots(1, 3, figsize=(15,5))\n",
+    "\n",
+    "# create first subplot \n",
+    "axs[0].scatter(X[:,0], y)\n",
+    "axs[0].set_title('first feature $x_{1}$ vs. label $y$')\n",
+    "axs[0].set_xlabel('$x_{1}$ \\nmean gray level of pixel neighborhood')\n",
+    "axs[0].set_ylabel('$y$')\n",
+    "\n",
+    "# create second subplot \n",
+    "axs[1].scatter(X[:,1], y)\n",
+    "axs[1].set_xlabel('$x_{2}$ \\nmedian gray level of pixel neighborhood')\n",
+    "axs[1].set_title('second feature $x_{2}$ vs. label $y$')\n",
+    "axs[1].set_ylabel('$y$')\n",
+    "\n",
+    "# create second subplot \n",
+    "axs[2].scatter(X[:,2], y)\n",
+    "axs[2].set_xlabel('$x_{3}$ \\nrandom feature')\n",
+    "axs[2].set_title('third feature $x_{3}$ vs. label $y$')\n",
+    "axs[2].set_ylabel('$y$')\n",
+    "\n",
+    "# display the figure containing two subplots \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "c26fe7ac97213719a60e2181c82340eb",
+     "grade": false,
+     "grade_id": "cell-26aa2ceb66c02fb5",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "Take a look at the scatter plots and note the relationship between each feature and label. \n",
+    "How do the scatter plots of the features $x_{1}$ and $x_{2}$ (mean and median gray level of the pixel neighborhood) differ from the scatter plot using the random feature $x_{3}$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "ac601032e105b45a054a52293e4ba20a",
+     "grade": false,
+     "grade_id": "cell-d8ccaaf344192e57",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='drawplot'></a>\n",
+    "<div class=\" alert alert-warning\"><b>Student Task.</b> Scatter Plots.\n",
+    "\n",
+    "<p>Create a scatterplot with x-axis (horizontal axis) representing the fourth feature $x^{(i)}_{4}$ and $y$-axis (vertical axis) representing the label values $y^{(i)}$ for each of the $m=10$ labeled data points $\\big(\\mathbf{x}^{(i)},y^{(i)}\\big)$. \n",
+    "</p>\n",
+    "<p><b>Hint:</b>  Remember, indexing in python starts from 0, not 1.</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "564eadc91c07adf680dfcbe3b7445df5",
+     "grade": false,
+     "grade_id": "cell-6ae0e9e3279dd417",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.57822555  0.10749794 -0.76404783 -0.77518851  1.38384717  0.76038508\n",
+      " -0.28564551  0.53836748 -2.08389663  0.93778171]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt \n",
+    "\n",
+    "def studentScatterPlot(X, y):\n",
+    "    fig, axes = plt.subplots(1, 1, figsize=(8, 4))\n",
+    "    ### STUDENT TASK ###\n",
+    "    # get the correct feature from X and save to X_4\n",
+    "    # X_4 = ...\n",
+    "    # use X_4 to create the scatter plot\n",
+    "    # axes.scatter ... \n",
+    "    # YOUR CODE HERE\n",
+    "    \n",
+    "    #print(X)\n",
+    "    \n",
+    "    X_4 = X[:,3]\n",
+    "    print(X_4)\n",
+    "    \n",
+    "    axes.scatter(X_4, y)\n",
+    "    \n",
+    "    \n",
+    "    axes.set_title('feature $x_{4}$ vs. label $y$')\n",
+    "    axes.set_xlabel(r'$x_{4}$')\n",
+    "    axes.set_ylabel('$y$')\n",
+    "    return X_4, axes\n",
+    "\n",
+    "X,y = GetFeaturesLabels(10,10)   # read in features and labels\n",
+    "\n",
+    "axes = studentScatterPlot(X, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "978858418660437f8d903ac54cdefcdd",
+     "grade": true,
+     "grade_id": "cell-f1705f6e7a1a7c67",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.57822555  0.10749794 -0.76404783 -0.77518851  1.38384717  0.76038508\n",
+      " -0.28564551  0.53836748 -2.08389663  0.93778171]\n",
+      "sanity check tests passed!\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# this cell constains visible tests (sanity checks) and \n",
+    "# hidden tests which are used for grading student solutions \n",
+    "\n",
+    "X, y = GetFeaturesLabels(10,10)   # read in features and labels\n",
+    "X_4, axes = studentScatterPlot(X, y)\n",
+    "assert X_4.shape == (10,), \"'X_4' has wrong dimensions.\"\n",
+    "assert X_4[1] > X_4[2], \"'X_4' values are not correct\"\n",
+    "assert X_4[9] > X_4[8], \"'X_4' values are not correct\"\n",
+    "\n",
+    "\n",
+    "print('sanity check tests passed!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "06cd14711b4f1fc4f584a12b50e97fcc",
+     "grade": false,
+     "grade_id": "cell-ba1d8078dc67137d",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "## Linear Regression \n",
+    "\n",
+    "We try to predict the label $y$ of a data point (representing a pixel in an aerial photograph) \n",
+    "based on $n$ properties or features $\\mathbf{x}=(x_{1},\\ldots,x_{n})^{T} \\in \\mathbb{R}^{n}$ of that data point. To this end, we try to find (or learn) a predictor function $h(\\mathbf{x})$ such that $y \\approx h(\\mathbf{x})$. \n",
+    "\n",
+    "Linear regression uses linear predictor functions of the form \n",
+    "\n",
+    "\\begin{equation*}\n",
+    "h^{(\\mathbf{w})}(\\mathbf{x}) = \\mathbf{w}^{T} \\mathbf{x} \\mbox{ for some weight vector } \\mathbf{w} \\in \\mathbb{R}^{n}.\n",
+    "\\label{eq1}\n",
+    "\\tag{1}\n",
+    "\\end{equation*}\n",
+    "\\\n",
+    "\\\n",
+    "Note that for vectors $\\mathbf{w}=\\big(w_{1},\\ldots,w_{n}\\big)^{T} \\in \\mathbb{R}^{n}$ and $\\mathbf{x}=\\big(x_{1},\\ldots,x_{n}\\big)^{T}\\in \\mathbb{R}^{n}$,\n",
+    "\\begin{equation*}\n",
+    "\\mathbf{w}^{T} \\mathbf{x} =  w_{1} x_{1}+...+w_{n} x_{n} = \\sum_{r=1}^{n} w_{r} x_{r}.\n",
+    "\\end{equation*} "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "ae8f77b34a30f4579cc923bb4aef6bb6",
+     "grade": false,
+     "grade_id": "cell-5eb2e2fdcba5ac36",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<a id='drawplot'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "    \n",
+    "### Demo. Build a Linear Predictor\n",
+    "\n",
+    "The code snippet below shows how to use the `LinearRegression` class from the Python library `scikit-learn` to learn an optimal predictor from the set of linear predictors $h(\\mathbf{x}) = \\mathbf{w}^{T} \\mathbf{x}$ and find its  optimal weight vector $\\mathbf{w}_{\\rm opt}$ .\n",
+    "\n",
+    "The linear regression model is fitted to the input data $\\mathbf{X}$ by calling the function `LinearRegression.fit()`. The function finds the optimal weight vector $\\mathbf{w}_{\\rm opt}$ and stores it in the attribute `LinearRegression.coef_` of the `LinearRegression` object.\n",
+    "\n",
+    "You can find the documentation of the `LinearRegression` class under [this link](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn-linear-model-linearregression). \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\mathbf{w}_{\\rm opt} =$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.66131446]\n",
+      " [0.34372099]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# the class \"LinearRegression\" from the library scikit-learn allows to find \n",
+    "# linear predictors \n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "# import some convenient tools for plotting data \n",
+    "from IPython.display import display, Math\n",
+    "\n",
+    "# read in features and labels (grayscale values) of uncorrupted pixels \n",
+    "X,y = GetFeaturesLabels(1000, 2)   \n",
+    "\n",
+    "# create a LinearRegression object with no intercept term\n",
+    "# this object represents the space of predictor functions h(x) = w^{T}x\n",
+    "reg = LinearRegression(fit_intercept=False) \n",
+    "\n",
+    "# find optimal weight vector   \n",
+    "reg = reg.fit(X, y)\n",
+    "\n",
+    "# we can read out the optimal weight vector using reg.coef_ \n",
+    "optimal_weight = reg.coef_\n",
+    "\n",
+    "# reshape the numpy array to have dimension (n,1) \n",
+    "optimal_weight = optimal_weight.reshape(-1,1)\n",
+    "\n",
+    "# plot the optimal weight vector entries \n",
+    "display(Math(r'$\\mathbf{w}_{\\rm opt} ='))\n",
+    "print(optimal_weight)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "d0432f0bbd1e346ba68eb6d585c04a7a",
+     "grade": false,
+     "grade_id": "cell-8874be7c5bb9deb6",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "Now that we have trained our linear predictor, let's use it to predict the grayscale value of the corrupted pixels in our image."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "84b054083c37226e8082a7af26b51f9a",
+     "grade": false,
+     "grade_id": "cell-7cedcde8decf3648",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<a id='handsondata'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "    \n",
+    "### Demo. Display Recovered Pixels\n",
+    "<p>    \n",
+    "The code snippet below reads in a weight vector $\\mathbf{w} \\in \\mathbb{R}^{n}$ that defines a linear predictor $h(\\mathbf{x}) = \\mathbf{w}^{T} \\mathbf{x}$ for the grayscale value $y$ of a pixel. The prediction is based on the features $\\mathbf{x} = \\big(x_{1},\\ldots,x_{n}\\big)^{T}$ that characterize a pixel. \n",
+    "\n",
+    "These features are obtained by the neighbouring pixels of a given \"target\" pixel (which might be corrupted). In order to define the neighbourhood also for border pixels, we need to agument the image by stripes of all-zero pixels [in the same way as in function `GetFeaturesLabels()`]. \n",
+    "\n",
+    "The grayscale value of each corrupted pixel (which has grayscale value 0) is replaced by the prediction $\\hat{y} = h(\\mathbf{x}) = \\mathbf{w}^{T} \\mathbf{x}$. The resulting (recovered) image, with black pixels replaced by grayscale $\\hat{y}$, is then depicted. \n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "bb4c2775d464e836acc6dbaf206374df",
+     "grade": false,
+     "grade_id": "cell-496ca9814ca17649",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def DisplayRecoveredPixels(w):\n",
+    "    \n",
+    "    # w - optimal weights\n",
+    "    # w is 1-D np.array or scalar \n",
+    "\n",
+    "    # filename of image file containing corrupted pixels\n",
+    "    corrupted = '/coursedata/R2_Regression/SomePhotoCorrupted.bmp'\n",
+    "\n",
+    "    # read in the corrupted aerial photograph as grayscale (second argument 0) \n",
+    "    # and store it in variable img\n",
+    "    Photo = cv2.imread(corrupted, 0)\n",
+    "    \n",
+    "    # make sure that photograph is represented by 100 by 100 pixels \n",
+    "    Photo = cv2.resize(Photo, (100, 100))\n",
+    "    \n",
+    "    imgwidth = Photo.shape[0]\n",
+    "    imgheight = Photo.shape[1]\n",
+    "    \n",
+    "    # determine corrupted pixels (those whose grayscale = 0)\n",
+    "    error_idx = np.where(Photo < 1)\n",
+    "    \n",
+    "    # store the vertical coordinate (row index) of corrupted pixels in numpy array rows_err \n",
+    "    rows_err = error_idx[0]\n",
+    "    # store the horizontal coordinate (column index) of corrupted pixels in numpy array cols_err \n",
+    "    cols_err = error_idx[1]\n",
+    "    # determine number of corrupted pixels \n",
+    "    errsize = rows_err.shape[0]\n",
+    "    \n",
+    "    # create a numpy array x1 and x2 which we use to store the features vectors of corrupted pixels\n",
+    "    x1 = np.zeros((errsize,1)) \n",
+    "    x2 = np.zeros((errsize,1))\n",
+    "    \n",
+    "    # set pads for defining pixel neighborhood and augmenting the image\n",
+    "    wp = 1\n",
+    "    hp = 1   \n",
+    "\n",
+    "    # augment image with stripes such that we can also define neighborhoods of border pixels \n",
+    "    tmp = np.vstack((np.zeros((wp, imgwidth)), Photo, np.zeros((wp, imgwidth))))\n",
+    "    augmented = np.hstack((np.zeros((2*wp + imgheight, hp)), tmp, np.zeros((2*wp + imgheight, hp))))\n",
+    "   \n",
+    "    # calculate the mean and median gray scale value of a pixel neighborhood \n",
+    "    # here we define 3x3 pixel matrix surrounding a pixel as its neighborhood\n",
+    "    for iter_datapoint in range(errsize): \n",
+    "        # determine row index in augmented image (add offset wp)\n",
+    "        row = rows_err[iter_datapoint]+wp\n",
+    "        # determine column index in augmented image (add offset hp)\n",
+    "        col = cols_err[iter_datapoint]+hp\n",
+    "        \n",
+    "        # calculate the feature of a data point (pixel) - the mean gray level of the neighborhoud \n",
+    "        neighbors = augmented[(row-wp):(row+wp+1), (col-hp):(col+hp+1)]\n",
+    "\n",
+    "        # zero values are exluded from mean calculation\n",
+    "        x1[iter_datapoint] = np.mean(neighbors[neighbors != 0])\n",
+    "        x2[iter_datapoint] = np.median(neighbors[neighbors != 0])\n",
+    "        \n",
+    "    np.random.seed(30) # this is done so that every time that below np.random.randn is called, \n",
+    "                       # it produces the same output. \n",
+    "                       # this is needed for testing purposes\n",
+    "    \n",
+    "    # define number (n) and length(m) of \"extra (random) features\" \n",
+    "    m = errsize # length of feature vector    \n",
+    "    # number of features (equal to length of weight vector)\n",
+    "    n = w.shape[0]\n",
+    "    \n",
+    "    # lets add some \"extra (random) features\" here\n",
+    "    X = np.hstack((x1,x2,np.random.randn(m,n))) \n",
+    "    X = X[:,:n]\n",
+    "    \n",
+    "    # compute predicted label values (grayscale level) of corrupted pixels \n",
+    "    y_hat = X.dot(w)\n",
+    "\n",
+    "    # the range of gray scale value (0-255) might change after multiplication with weight w\n",
+    "    # therefore we need to remove all values smaller than 0 and bigger than 255\n",
+    "    y_hat = np.clip(y_hat,0,255)\n",
+    "    # convert floats to integers\n",
+    "    y_hat.astype(int)\n",
+    "\n",
+    "    # copy pixels of corrupted image into numpy array \"reconstruction\"\n",
+    "    reconstruction = Photo.copy()\n",
+    "\n",
+    "    for iter_datapoint in range(errsize): \n",
+    "        row = rows_err[iter_datapoint]\n",
+    "        col = cols_err[iter_datapoint]\n",
+    "        # replace corrupted pixels with predicted grayscale values\n",
+    "        reconstruction[row,col]=  y_hat[iter_datapoint]\n",
+    "\n",
+    "    # display corrupted photo along with the reconstructed photo \n",
+    "    fig, ax = plt.subplots(1, 2, figsize=(20, 10))\n",
+    "    \n",
+    "    # grayscale plot of corrupted Photo \n",
+    "    ax[0].imshow(Photo,cmap='gray')\n",
+    "    # grayscale plot of reconstructed Photo \n",
+    "    ax[1].imshow(reconstruction, cmap='gray')\n",
+    "    \n",
+    "    ax[0].set_title(\"corrupted aerial photograph\",fontsize=20)\n",
+    "    ax[1].set_title(\"reconstructed aerial photograph\",fontsize=20)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "2aaa1e558ab4673c4b864257e6ab2258",
+     "grade": false,
+     "grade_id": "cell-2f09c5c9566fe241",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the aearial photo with reconstructed corrupted pixels\n",
+    "DisplayRecoveredPixels(optimal_weight)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "fdfa16f797a2bed1ab0a2a2c642fa6f5",
+     "grade": false,
+     "grade_id": "cell-ba7892d8ce8fd0ba",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "## Loss function\n",
+    "\n",
+    "To measure the quality of a particular predictor $h^{(\\mathbf{w})}(\\mathbf{x}) = \\mathbf{w}^{T} \\mathbf{x}$, obtained for some particular choice for the weight vector $\\mathbf{w} \\in \\mathbb{R}^{n}$, we apply it to labeled data points, i.e., data points for which we know the correct label value $y$. For a labeled data point with features $\\mathbf{x}^{(i)}$, we can compare its label $y^{(i)}$ with the predicted label $h^{(\\mathbf{w})}(\\mathbf{x}^{(i)}) = \\mathbf{w}^{T} \\mathbf{x}^{(i)}$. \n",
+    "\n",
+    "The prediction $h^{(\\mathbf{w})}(\\mathbf{x}^{(i)})$ will typically incur a non-zero __prediction error__ $y^{(i)} - h^{(\\mathbf{w})}(\\mathbf{x}^{(i)})$. To measure the error or **loss** incurred by the prediction $\\hat{y}=h(\\mathbf{x})$ (which is the result of applying the predictor function $h(\\cdot)$ to the features $\\mathbf{x}$), we need to define a loss function $\\mathcal{L}(y,\\hat{y})$. In principle, the loss function can be chosen freely by the ML scientist or engineer based on the application at hand. \n",
+    "\n",
+    "For numeric labels $y$, e.g. the label is a real number, a popular choice for the loss is the [squared error loss](https://scikit-learn.org/stable/modules/model_evaluation.html#mean-squared-error) $\\mathcal{L}(y,\\hat{y}) = (y-\\hat{y})^{2}$. Note that we can only evaluate the loss if we know the true label $y$ of a data point. Given a set of labeled data points $\\big(\\mathbf{x}^{(i)},y^{(i)}\\big)$, we can compute the average loss \n",
+    "\\begin{equation*}\n",
+    " \\mathcal{E} (\\mathbf{w}) = (1/m) \\sum^{m}_{i=1}(y^{(i)} - \\mathbf{w}^{T} \\mathbf{x}^{(i)})^2\n",
+    "\\label{eq2}\n",
+    "\\tag{2}\n",
+    "\\end{equation*}\n",
+    "for a particular predictor $h^{(\\mathbf{w})}(\\mathbf{x}) = \\mathbf{w}^{T} \\mathbf{x}$.\n",
+    "\n",
+    "\n",
+    "\n",
+    "The optimal weight vector $\\mathbf{w}_{\\rm opt}$ is any weight vector which achives the minimum value of $ \\mathcal{E} (\\mathbf{w})$, i.e., $$\\mathcal{E} (\\mathbf{w}_{\\rm opt})= \\min_{\\mathbf{w} \\in \\mathbb{R}^{n}}\\mathcal{E} (\\mathbf{w})$$\n",
+    "An optimal predictor is then obtained as $h(\\mathbf{x}) = \\mathbf{w}_{\\rm opt}^{T} \\mathbf{x}$. The average loss \n",
+    "\n",
+    "$$\\mathcal{E} (\\mathbf{w}_{\\rm opt}) = (1/m) \\sum^{m}_{i=1}(y^{(i)} - \\mathbf{w}_{\\rm opt}^{T} \\mathbf{x}^{(i)})^2$$\n",
+    " \n",
+    "incurred by the optimal predictor $h(\\mathbf{x}) = \\mathbf{w}_{\\rm opt}^{T} \\mathbf{x}$ is also known as the **training error**. The training error is the average loss of the linear predictor which is optimal the labeled data points (the **training data**) $\\big(\\mathbf{x}^{(i)},y^{(i)}\\big)$ for $i=1,\\ldots,m$. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "7c2de11c12e97d2997e12aeed033db03",
+     "grade": false,
+     "grade_id": "cell-33d93b85f8f16201",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "The animation below illustrates the process of learning the optimal linear predictor (weights) by changing the weight vector $\\mathbf{w}$ towards the minimum of the mean squared error function (2). The movement towards the minimum of the function is obtained by the [gradient descent algorithm](https://en.wikipedia.org/wiki/Gradient_descent). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "fa76a37c2ae298c70657260b57f5d41c",
+     "grade": false,
+     "grade_id": "cell-42fd0d6c3bfc40c1",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "![SegmentLocal](../../../coursedata/R2_Regression/GD.gif \"segment\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "5084a2aad5b7f44cceccd0c80888fb90",
+     "grade": false,
+     "grade_id": "cell-049bdf44b143edcb",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='drawplot'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "    \n",
+    "### Demo. Squared Error Loss\n",
+    "<p>\n",
+    "The code snippet below shows how to evaluate the quality of the linear predictor $h(\\mathbf{x}) = \\mathbf{w}^{T} \\mathbf{x}$ by using the squared error loss.  \n",
+    "</p>\n",
+    "\n",
+    "The optimal weight vector $\\mathbf{w}_{\\rm opt}$ can be found by the function `LinearRegression.fit()`. The resulting optimal weight vector is stored in the variable `LinearRegression.coef_`. Using the optimal weight vector, we compute the training error \n",
+    "\\begin{equation}\n",
+    "(1/m) \\sum_{i=1}^{m} \\big( y^{(i)} - \\mathbf{w}_{\\rm opt}^{T} \\mathbf{x}^{(i)} \\big)^{2} \n",
+    "\\end{equation}\n",
+    "and store it in the variable `training_error`. \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "a7cdf19ef5392eb2aae909397551d103",
+     "grade": false,
+     "grade_id": "cell-577688b1df8c48f9",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "The resulting average squared error (training error) is  742.2685991473817\n"
+     ]
+    }
+   ],
+   "source": [
+    "# the class \"LinearRegression\" from the library scikit-learn allows to find \n",
+    "# linear predictors that minimize the average squared error loss\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "# the function \"mean_squared_error\" allows to compute the average squared error \n",
+    "# between two lists of label values (one list with true label values \n",
+    "# and the other list with predicted label values)\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "# read in features and labels (grayscale values) of uncorrupted pixels \n",
+    "X,y = GetFeaturesLabels(1000, 3)   \n",
+    "# create a LinearRegression object with no intercept term\n",
+    "# this object represents the space of predictor functions h(x) = w^{T}x\n",
+    "reg = LinearRegression(fit_intercept=False) \n",
+    "\n",
+    "# the method fit() determines the weight vector w such that the \n",
+    "# average squared error between predicted label w^{T}x and true \n",
+    "# label y is minimized for the labeled data points \n",
+    "reg = reg.fit(X, y)\n",
+    "\n",
+    "# after the optimal weight w has been determined determine the \n",
+    "# corresponding average squared error (mean squared errror) on \n",
+    "# the training set (which has been used to optimize the weight!)\n",
+    "training_error = mean_squared_error(y, reg.predict(X))\n",
+    "\n",
+    "# display training error \n",
+    "print(\"\\nThe resulting average squared error (training error) is \", training_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "33410d1703ba7b6d1a0507259bccb1d3",
+     "grade": false,
+     "grade_id": "cell-a5ee14879390235c",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='varying_features'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <p><b>Student Task</b> Varying Number of Features. </p>\n",
+    "    \n",
+    "In principle, we can freely choose how many of the available features $x_{1}, x_{2},\\ldots,x_{n}$ \n",
+    "of a data point to use for predicting the label $y$. You are now to explore the effect of using a varying number $r \\leq n$ of features on the resulting training error of linear regression. For each $r=1,2,\\ldots,10$, fit a linear model (using `LinearRegression(fit_intercept=False)`) to the labeled dataset (using $m=10$ data points) by using only the first $r$ features $x_{1},...,x_{r}$ of a data point. \n",
+    "<br />    \n",
+    "- You can get the first $r$ features and label of $m$ labeled data points using `GetFeaturesLabels(m,r)`.<br />\n",
+    "- For each value of $r$, determine the resulting training error (using the Python function `mean_squared_error()`) of the fitted linear model. <br />\n",
+    "- The results should be stored in a numpy array `linreg_error`, such that for the error for $r=1$ is stored in the first element, the error for $r=2$ in the second element etc..<br />\n",
+    "\n",
+    "<p><b>HINT: In order to avoid mistakes, start looping from 0 (and not from 1)!</b></p>\n",
+    "<p><b>HINT: Don't use `GetFeaturesLabels` again inside the loop.</b></p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "06281192cc696e492241d7cbae09dbe9",
+     "grade": false,
+     "grade_id": "cell-adb3039336234c0d",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BBg+GH3+MuxoRERFJoIBTWXXqwPXXhzFx7rwz7mpEREQkgQJOVRx7LBx5JIwYAStWxF2NiIiIRBRwqsIsTOHwzTcwenTc1YiIiEhEAaeqOncOk3GOHRvmqhIREZHYKeBUh5Ejw9mcoUPjrkRERERQwKke228PF14I48fDjBlxVyMiIlLjKeBUlyFDoGVLDf4nIiKSARRwqkuTJjB8OEydCs8+G3c1IiIiNVrKAo6Z3WNmS81sdhnPDTQzN7OWCesuNbN5ZvaRmfVIVV0p9ec/wy67wCWXwPr1cVcjIiJSY6XyDM59wDGlV5pZW6A78FnCug5AX6BjtM9tZlY7hbWlRl4e/P3vMGcO3HNP3NWIiIjUWCkLOO7+CvBNGU+NAS4BEjuq9AYecfe17r4QmAd0SVVtKXX88XDIITBsGKxcGXc1IiIiNVJa++CY2XHAF+7+Xqmn2gCfJzwujtaV9Rpnm1mRmRUtW7YsRZVWQcngf0uWhFsRERFJu7QFHDNrAFwODCvr6TLWlXkpkruPc/dCdy/Mz8+vzhKrzwEHQJ8+IeB8+WXc1YiIiNQ46TyDsxPQHnjPzBYBBcA7ZrYt4YxN24RtC4DsTgbXXAPr1oWmKhEREUmrtAUcd3/f3Vu5ezt3b0cINfu6+1fARKCvmdU1s/bALsBb6aotJXbcEc47L3Q2fv/9uKsRERGpUVJ5mfgEYDqwm5kVm9mZm9vW3T8AHgM+BCYBA9z9p1TVljaXXx7Gx7nkkrgrERERqVHMs3jU3cLCQi8qKoq7jPLdeCNcfDG8+CJ07x53NSIiIjnFzGa4e2Hp9RrJONUGDID27cMUDj9l/0kpERGRbKCAk2p168K118KsWfDgg3FXIyIiUiMo4KTDSSdBly4wdCisXh13NSIiIjlPAScdSgb/++ILGDMm7mpERERyngJOunTrFqZxGD06jHIsIiIiKaOAk05//zusWQMjRsRdiYiISE5TwEmnXXeFP/8Zxo2DuXPjrkZERCRnKeCk2/Dh0KABDB4cdyUiIiI5SwEn3fLz4dJLYeJEmDYt7mpERERykgJOHC68EAoKwuB/GzbEXY2IiEjOUcCJQ/36MGoUFBXBo4/GXY2IiEjOUcCJy6mnQqdOoblqzZq4qxEREckpCjhxqVUrDP736adwyy1xVyMiIpJTFHDidNRRcOyxMHIkLF8edzUiIiI5QwEnbtddBytXhpAjIiIi1UIBJ2577gn9+8Ott8L8+XFXIyIikhMUcDLBVVdBXl7ocCwiIiJVpoCTCVq3hkGD4PHHYfr0uKsRERHJego4mWLgQNh223DrHnc1IiIiWU0BJ1M0ahSaqt54A55+Ou5qREREspoCTiY54wzo2DFMxPnjj3FXIyIikrUUcDJJnTrhsvF58+DOO+OuRkREJGsp4GSaY48NAwCOGAErVsRdjYiISFZSwMk0ZnD99fDNNzB6dNzViIiIZCUFnEzUuTOcdhqMHRvmqhIREZEKSVnAMbN7zGypmc1OWHe9mc01s1lm9rSZNU147lIzm2dmH5lZj1TVlTVGjgxnc4YOjbsSERGRrJPKMzj3AceUWjcZ2NPd9wY+Bi4FMLMOQF+gY7TPbWZWO4W1Zb62beGii2D8eJgxI+5qREREskrKAo67vwJ8U2rdi+6+Pnr4X6Agut8beMTd17r7QmAe0CVVtWWNwYOhZUsN/iciIlJBcfbB6Q88H91vA3ye8FxxtG4TZna2mRWZWdGyZctSXGLMmjSBK6+EqVPh2WfjrkZERCRrxBJwzOxyYD3wUMmqMjYr85SFu49z90J3L8zPz09ViZnj7LNh113hkktg/fotby8iIiLpDzhm1g/oBfzB/ed2l2KgbcJmBcCX6a4tI+Xlwd//DnPmwN13x12NiIhIVkhrwDGzY4DBwHHuvjrhqYlAXzOra2btgV2At9JZW0br3RsOOQSGD4eVK+OuRkREJOOl8jLxCcB0YDczKzazM4FbgMbAZDObaWZ3ALj7B8BjwIfAJGCAu/+UqtqyjhnccAMsWRIGARQREZFymWfx1TmFhYVeVFQUdxnp07cvTJwIn3wCbcrsgy0iIlKjmNkMdy8svV4jGWeTa64JHY2HDYu7EhERkYymgJNNdtwRzjsP7r0XZs2KuxoREZGMpYCTbS6/PIyPc8klcVciIiKSsRRwsk3z5nDFFfDCC/Dii3FXIyIikpEUcLLRgAHQvj0MGgQ/6WIzERGR0hRwslHdunDttaEfzoMPxl2NiIhIxlHAyVYnnQRdusDQobB69Za3FxERqUEUcLJVyeB/X3wBY8bEXY2IiEhGUcDJZt26wfHHw+jRYZRjERERARRwst/f/w5r1sCIEXFXIiIikjEUcLLdrrvCn/8M48bB3LlxVyMiIpIRFHBywfDh0KABDB4cdyUiIiIZQQEnF+Tnw6WXhok4p02LuxoREZHYKeDkigsvhIICGDgQNmyIuxoREZFYKeDkivr1YdQoKCqCRx+NuxoREZFYKeDkklNPhU6dQnPVmjVxVyMiIhIbBZxcUqtWGPzv00/hllvirkZERCQ2Cji55qijoGdPGDkSli+PuxoREZFYKODkouuug5UrQ8gRERGpgRRwclHHjnDmmXDrrTB/ftzViIiIpJ0CTq4aMQK22ip0OBYREalhFHByVevWMGgQPP44TJ8edzUiIiJppYCTyy6+GLbdNgz+5x53NSIiImmjgJPLGjWCq6+GN96Ap56KuxoREZG0SVnAMbN7zGypmc1OWNfczCab2SfRbbOE5y41s3lm9pGZ9UhVXTXOGWeETsdDhsCKFTqTIyIiNUKdFL72fcAtwAMJ64YAU9x9tJkNiR4PNrMOQF+gI7Ad8B8z29Xdf0phfTVD7dpw/fVhbJxmzSAvD1q2hBYtwlLW/dLrmjQJgwiKiIhkiZQFHHd/xczalVrdGzg8un8/MBUYHK1/xN3XAgvNbB7QBVDv2OpwzDHw/PMwezZ8/XUYALDkds6cX+7/tJk8WavWL4En2VDUrBnUSWV+FhER2bx0/wbaxt0XA7j7YjNrFa1vA/w3YbviaJ1UB7MQco45ZvPbuMN3320agBLvl9wuXAhvvx3ur127+dds1qxioahFi3Bpu4iISBVlyp/YVsa6MjuLmNnZwNkA22+/fSprqlnMQlNUkyaw007J7eMOq1eXH4pK7i9eDO+/Hx6vWrX512zUqPwAVHK/TRvYY4/qee8iIpJzyg04ZlYLmOXue1bT8ZaYWevo7E1rYGm0vhhom7BdAfBlWS/g7uOAcQCFhYXqMRsnM2jYMCw77JD8fmvW/BJ+yjpDlHh/3rxw/9tvN32da67RQIYiIlKmcgOOu28ws/fMbHt3/6wajjcR6AeMjm6fSVj/sJndSOhkvAvwVjUcTzJRvXrhDEybCrRCrlsH33zzSwD65z9h6FA4+GA49NDU1SoiIlkpmSaq1sAHZvYW8HPbgrsfV95OZjaB0KG4pZkVA8MJweYxMzsT+Aw4MXqtD8zsMeBDYD0wQFdQyUby8mCbbcIC0KkTzJoFJ58MM2dCfn689YmISEYx38K4KGZ2WFnr3X1aSiqqgMLCQi8qKoq7DInLzJlw4IFwxBHw7LO6lF1EpAYysxnuXlh6/RZ/I0RBZi7QOFrmZEK4EaFTJxgzBiZNCmP9iIiIRLYYcMzsJEJ/mBOBk4A3zeyEVBcmkpRzzoETT4TLL4fXX4+7GhERyRDJNFG9B3R396XR43zgP+6+TxrqK5eaqAQIV1jtt18Yk2fmzHApuYiI1AiVbqICapWEm8jyJPcTSY8mTeCxx2DpUujXDzZsiLsiERGJWTJBZZKZvWBmp5vZ6cCzwHOpLUukgvbdF/7xj9DZ+MYb465GRERitsXLxN19kJn9DjiEMOLwOHd/OuWViVTUgAHw8sth8L9DDglXWImISI20pZGMawMvuPvRwFPpKUmkkszg7rvD2Zw+feDdd6F587irEhGRGJTbRBUNtrfazJqkqR6RqmnaFB59NMx9dcYZYb4sERGpcZLpg7MGeN/M7jazm0qWVBcmUmn77x/GxZk4EcaOjbsaERGJQTJTNTwbLSLZ4/zzYepUGDw4zFfVpUvcFYmISBol0wenu7ufmqZ6RKqHGdxzD3Tu/Et/nKZN465KRETSJJk+OPlmtlWa6hGpPs2ahf44xcXQv7/644iI1CDJ9MFZBLxuZleY2d9KlhTXJVI9DjgARo+Gp5+GW26JuxoREUmTZALOl8C/o20bJywi2eFvf4Pf/AYuvhg0tYeISI2wxbmoft7QrKG7r0pxPRWiuagkad98E2Yfz8uDd94J0zuIiEjWq/RcVGbW1cw+BOZEj/cxs9tSUKNI6jRvDo88Ap9+Cmedpf44IiI5LpkmqrFAD8Ikm7j7e8ChqSxKJCUOOgiuuQaeeAJuvz3uakREJIWSmhXc3T8vteqnFNQiknoDB8Kxx8JFF4VLx0VEJCclE3A+N7ODADezrcxsIFFzlUjWqVULHngA8vPhpJPgu+/irkhERFIgmYBzDjAAaAMUA52ixyLZqWXL0B9n4UI4+2z1xxERyUFbDDju/rW7/8Hdt3H3Vu5+qrsvT0dxIilzyCFw9dVhIMBx4+KuRkREqllSfXBEctLgwdCjB1xwAbz3XtzViIhINVLAkZqrpD9O8+ahP87KlXFXJCIi1UQBR2q2Vq1gwgSYNw/OOUf9cUREcsRmZxPf0nxT7n5j9ZcjEoPDDoMRI+CKK+CII8JAgCIiktXKO4PTeAtLpZnZRWb2gZnNNrMJZlbPzJqb2WQz+yS6bVaVY4hUyKWXwtFHw3nnwfvvx12NiIhUUdJzUVXbAc3aAK8BHdz9BzN7DHgO6AB84+6jzWwI0MzdB5f3WpqLSqrVkiVhvqqmTeHtt6FRo7grEhGRLajKXFT1zGyAmd1mZveULFWspw5Q38zqAA0IM5b3Bu6Pnr8fOL6KxxCpmG22gYcego8+ggEa6klEJJsl08n4QWBbwnxU04ACoNKXm7j7F8ANwGfAYuBbd38R2MbdF0fbLAZalbW/mZ1tZkVmVrRs2bLKliFStiOPhGHDwtVV990XdzUiIlJJyQScnd39CmCVu98P/BrYq7IHjPrW9AbaA9sBDc3s1GT3d/dx7l7o7oX5+fmVLUNk80o6G//1r/DBB3FXIyIilZBMwFkX3a4wsz2BJkC7KhzzaGChuy9z93XAU8BBwBIzaw0Q3S6twjFEKq927dBU1bhxGB9n1aq4KxIRkQpKJuCMi866DAUmAh8C11XhmJ8BB5pZAzMz4CjC5J0TgX7RNv2AZ6pwDJGqad06hJw5c8KVVSIiklU2Ow5OCXe/K7r7CrBjVQ/o7m+a2RPAO8B64F1gHNAIeMzMziSEoBOreiyRKjn6aLj8chg5MjRZnXZa3BWJiEiStniZuJldA1zn7iuix82Ai919aBrqK5cuE5eUW78ejjoKZsyAoiLYffe4KxIRkQSVvkwcOLYk3AC4+/+AntVZnEjGqlMHHn4Y6teHE0+E1avjrkhERJKQTMCpbWZ1Sx6YWX2gbjnbi+SWNm1g/HiYPTvMPC4iIhkvmYAzHphiZmeaWX9gMr8MyCdSM/ToEaZzuOuucEZHREQyWlJTNZjZsYSrnQx40d1fSHVhyVAfHEmr9etDZ+OZM0OfnF13jbsiEZEab3N9cLZ4FRWAuz8PPF/tVYlkkzp1YMKEMF/VSSfBf/8L9erFXZWIiJRhs01UZvZadLvSzL5LWFaa2XfpK1EkgxQUhGkc3nsPLroo7mpERGQzNhtw3P2Q6Laxu2+dsDR2963TV6JIhunZEwYNgjvugEcfjbsaEREpQ7mdjM2slpnNTlcxIllj1Cjo2hX+9CeYNy/uakREpJRyA467bwDeM7Pt01SPSHbIy4NHHgn9ck46CdasibsiERFJkMxl4q2BD8xsiplNLFlSXZhIxtt+e7j/fnj3XRg4MO5qREQkQTJXUY1IeRUi2eo3v4G//Q1uvBEOPxxOOCHuikREhOQm25yWjkJEsta118Jrr8GZZ0LnzrDTTnFXJCJS4+kycZGq2mqrcDVVrVrQpw+sXRt3RSIiNZ4uExepDu3awb33hhGOL7kk7mpERGq8ZDoZY3mt3JcAABhaSURBVGb7mtn5ZnaemXVOdVEiWen448NknDfdBE8/HXc1IiI12hYDjpkNI0yu2QJoCdxnZkNTXZhIVrruOigshP79YeHCuKsREamxkjmDczKwv7sPd/fhwIHAH1JblkiWKumPs2FD6I/z449xVyQiUiMlE3AWAYkzCtYF5qekGpFcsOOOcM898PbbMGRI3NWIiNRIyQSctYSB/u4zs3uB2cD3ZnaTmd2U2vJEstTvfw/nngtjxsBEjYspIpJuyQz093S0lJiamlJEcswNN8Abb8Dpp4fRjnfYIe6KRERqjGQG+rs/HYWI5Jy6dUN/nH33hb594ZVXwhxWIiKScslcRdXLzN41s2800J9IBe28M9x1F/z3v3D55XFXIyJSYyTTB2cs0A9ooYH+RCrhpJPgnHPg+uvh2WfjrkZEpEZIJuB8Dsx2d091MSI5a8wY2Gcf+OMf4fPP465GRCTnJdPJ+BLgOTObRriiCgB3vzFlVYnkmnr14PHHQ3+ck0+Gl19WfxwRkRRK5gzOKGA1YSycxglLpZlZUzN7wszmmtkcM+tqZs3NbLKZfRLdNqvKMUQyzi67wLhx8PrrMGxY3NWIiOS0ZM7gNHf3X1Xzcf8JTHL3E8xsK6ABcBkwxd1Hm9kQYAgwuJqPKxKvkrM3o0fDYYfBMcfEXZGISE5K5gzOf8ys2gKOmW0NHArcDeDuP7r7CqA3Yc4rotvjq+uYIhnln/+EvfaC006DL76IuxoRkZyUTMAZAEwyszXVdJn4jsAy4N7o8vO7zKwhsI27LwaIbluVtbOZnW1mRWZWtGzZsiqUIRKT+vXhscfghx/CGZ316+OuSEQk52wx4ESXhddy93rVdJl4HWBf4HZ37wysIjRHJcXdx7l7obsX5ufnV6EMkRjtvjvccQe8+ipceWXc1YiI5JxkBvozMzvVzK6IHrc1sy5VOGYxUOzub0aPnyAEniVm1jo6RmtgaRWOIZL5Tj0V+veHa66ByZPjrkZEJKck08n4NmADcCRwNfA9cCuwf2UO6O5fmdnnZrabu38EHAV8GC39gNHR7TOVeX2RrHLzzfDmm/CHP4QBARs3Tn7RZeYiIpuVTMA5wN33NbN3Adz9f9GVT1VxHvBQ9DoLgDMIZ5MeM7Mzgc+AE6t4DJHM16BBGB+nTx94+GFYuTL5Pjl161YsEJW3NGoEtZLpkicikh2SCTjrzKw24ABmlk84o1Np7j4TKCzjqaOq8roiWWmPPWDWrHDfHdauDUGnMsvXX8PChb88/v778JrJaNgwuSCUTGBq0ADMUveZiYhsQTIB5ybgaaCVmY0CTgCGprQqkZrKLIx6XK8eVEcnendYtWrjEPT998kHpi++2Pjx6tXJHbdOHWjXDnbaKUw4uvPOv9xv3z68PxGRFNpiwHH3h8xsBuHsigHHu/uclFcmIlVnFs66NGoErVtX/fV++im5gPS//4UzSfPmwfTp8F3CyBJmUFCwcehJvG1cpYHSRUSA5M7g4O5zgbkprkVEMl3t2tCkSViS5Q7Ll4ewM3/+xrfPPAOlx7Nq1ars8LPzztC8uZq+RCQpSQUcEZFKM4OWLcNy4IGbPv/ddyHwlISekgD08svw4IMbb9ukyaahp+R+69YKPyLyMwUcEYnX1ltD585hKe2HH0JTV+nwM2MGPPlkaDIrUb9+2U1eO+8MbduGfkEiUmPoX7yIZK769aFDh7CUtm4dfPbZpk1fH38MkybBmjW/bFunTujcXFb4ad8+XHIvIjlFAUdEslNeXggpO+206XMbNsCXX5bd7+e110JH6BJm4QxPWU1fO+0UOmiLSNZRwBGR3FOrVrhSq6AADj984+fcw3hBiaGn5P7TT4fnEm2zTTiDdMEFcNxx6ucjkiUUcESkZjELYwzl50PXrps+/+23m3Z6njYNjj8e9t8fRo6E7t0VdEQynAKOiEiiJk1g333DUmL9+nBF14gR0KMHdOsGo0aFWxHJSJp8RkRkS+rUgTPOgI8+gltvDWd1Dj00hJ233467OhEpgwKOiEiy6taFv/41BJwbbgiXq3fpEpqvSuYTE5GMoIAjIlJRDRrAxReHMXquvhqmToVOneDkk8NZHhGJnQKOiEhlNW4MQ4fCggVw6aXwr3+FK67694dFi+KuTqRGU8AREamq5s1Dp+MFC8Ll5A8/DLvuCgMGhPF4RCTtFHBERKpLq1Zw442hj86ZZ8K4cWGwwIEDN51UVERSSgFHRKS6FRTA7beH/jh9+sCYMbDjjnDFFbBiRdzVidQICjgiIqmy445w330wezb07BkGCWzfHq65Br7/Pu7qRHKaAo6ISKrtsQc8+ii8+24YHPDyy0P4GTMmzJguItVOAUdEJF06dYKJE2H6dNhnH/jb38LEnnfcAT/+GHd1IjlFAUdEJN0OPBAmT4aXX4Z27eAvf4Hdd4f77w/TQohIlSngiIjE5fDD4bXX4LnnoFkzOP102HPP0Jy1YUPc1YlkNQUcEZE4mcGxx0JRETz1VJj3qm9f6Nw5NGe5x12hSFZSwBERyQRm8NvfwnvvwfjxsGoV9O79S3OWgo5IhSjgiIhkktq14Q9/gDlz4P/9P1i8GH71KzjiiNCcJSJJiS3gmFltM3vXzP4dPW5uZpPN7JPotllctYmIxC4vD846Cz75BG6+OQwa2K3bL81ZIlKuOM/gXADMSXg8BJji7rsAU6LHIiI1W926cO65MH8+XHcdvPUW7L8//O53YQBBESlTLAHHzAqAXwN3JazuDdwf3b8fOD7ddYmIZKwGDWDQIFi4EEaMgClTYO+94ZRTwlkeEdlIXGdwxgKXAInXQW7j7osBottWZe1oZmebWZGZFS3T5HUiUtNsvTUMGxZmLh88GJ55JoyUfNZZ8OmncVcnkjHSHnDMrBew1N1nVGZ/dx/n7oXuXpifn1/N1YmIZIkWLeDaa0PT1bnnwoMPwi67hPuLF8ddnUjs4jiDczBwnJktAh4BjjSz8cASM2sNEN0ujaE2EZHssu22MHYszJsHZ5wBd94Z5rkaNAi+/jru6kRik/aA4+6XunuBu7cD+gIvufupwESgX7RZP+CZdNcmIpK12rYN4WbuXDjxRPjHP0LQGT4cvv027upE0i6TxsEZDXQ3s0+A7tFjERGpiJ12ggceCFdY9egBV10F7duH5qxVq+KuTiRtYg047j7V3XtF95e7+1Huvkt0+02ctYmIZLUOHeDxx2HGDDjoILjssnBGZ+xYWLMm7upEUs48i4f/Liws9CINeCUismVvvAFXXAEvvRQm9txmG2jYsOJLo0Zlr8/Li/sdSg1lZjPcvbD0+jpxFCMiIml20EFh7JyXXoKHHoLvvgtNVqtWwdKlv9wvWdaurdjr5+VVLjAls9StG+bqEqkABRwRkZrkyCPDsiXr18Pq1ZsGn4ou//sfFBdvvG716orVXLt2GOhwcwGobVsYMgTatKncZyI5SQFHREQ2VadOGFRw662r/7U3bIAffqh6eCo5+/TCC3D//TByJAwYEAKR1HgKOCIikl61av1y9qU6LFgQgs0FF4QryO68E/bbr3peW7JWJl0mLiIiUnE77gjPPQePPgpffAFdusCFF8LKlXFXJjFSwBERkexnBiedBHPmwDnnwE03hTm6nnoKsvhqYak8BRwREckdTZvCrbfC9OnQsiX8/vfQu7cmIq2BFHBERCT3HHAAFBXBDTeEy+M7dAjTV6xfH3dlkiYKOCIikpvq1IGLL4YPP4SjjoKBA6GwEN58M+7KJA0UcEREJLftsAM880zoj/P119C1K/z1r7BiRdyVSQop4IiISO4zg9/+NnRCPv/8cCn5HnuEK6/UCTknKeCIiEjN0bhxmHD0rbfCyMd9+0LPnmEsHckpCjgiIlLz7Ldf6Itz003w+uvQsSNcey38+GPclUk1UcAREZGaqXZtOO+80Gz161/DZZfBvvvCa6/FXZlUAwUcERGp2dq0gSeegH/9K4x+3K0b/OlP8M03cVcmVaCAIyIiAtCrV7ikfOBAuPde2H13GD9enZCzlAKOiIhIiYYN4frrYcaMMMfVaadB9+7w8cdxVyYVpIAjIiJS2j77wBtvwO23hxGR99oLRoyAtWvjrkySpIAjIiJSllq1wsSdc+fC734HV14Zgs/UqXFXJklQwBERESnPttvChAkwaRKsWwdHHAH9+sGyZXFXJuVQwBEREUlGjx4we3a4nPzhh0Mn5HvuUSfkDKWAIyIikqz69WHUKJg5M8xQfuaZcNhh4eorySgKOCIiIhXVsSNMmwZ33RXO6nTqBEOHwg8/xF2ZRBRwREREKqNWrXAGZ+7cMKfVqFHhaqsXX4y7MiGGgGNmbc3sZTObY2YfmNkF0frmZjbZzD6JbpuluzYREZEKa9UKHngApkwJ0z/06AGnnAJffRV3ZTVaHGdw1gMXu/sewIHAADPrAAwBprj7LsCU6LGIiEh2OPJIeO+9cDn5k0+GTsh33AEbNsRdWY2U9oDj7ovd/Z3o/kpgDtAG6A3cH212P3B8umsTERGpknr1YPhwmDUrTNz5l7/AIYfA++/HXVmNE2sfHDNrB3QG3gS2cffFEEIQ0Goz+5xtZkVmVrRMYxCIiEgm2m230GR1//3wySfQuTNccgmsWhV3ZTVGbAHHzBoBTwIXuvt3ye7n7uPcvdDdC/Pz81NXoIiISFWYwR//GDoh9+sX5rjq2BGefTbuymqEWAKOmeURws1D7v5UtHqJmbWOnm8NLI2jNhERkWrVogXcfTe88go0aBBmLT/hBPjii7gry2lxXEVlwN3AHHe/MeGpiUC/6H4/4Jl01yYiIpIy3bqFAQJHjQpncfbYA26+GX76Ke7KclIcZ3AOBk4DjjSzmdHSExgNdDezT4Du0WMREZHcsdVWYaqH2bOha1c4/3w48EB45524K8s5cVxF9Zq7m7vv7e6douU5d1/u7ke5+y7R7Tfprk1ERCQtdtopTN45YQJ8/jnsvz9cdBGsXBl3ZTlDIxmLiIjEwSyMgDx3Lpx9NowdG+a3evJJTeBZDRRwRERE4tS0Kdx+O7zxBjRrFjogd+kS+uko6FSaAo6IiEgm6NoVZswIV1wtXx6utjrwQHj+eQWdSlDAERERyRR5edC/P3z0UZipfOlS6NkzhJ9JkxR0KkABR0REJNPk5YWZyj/6CMaNg8WL4dhj4aCDwmzlCjpbpIAjIiKSqbbaCv70pzDdw513wpdfhtnKDzkEJk9W0CmHAo6IiEim22qrcKXVxx+HDsmffQa/+lUYPHDKFAWdMijgiIiIZIu6deGcc2DePLjtNvj0Uzj6aDjsMHjpJQWdBAo4IiIi2aZuXfjLX0LQueUWmD8fjjoKDj8cpk6Nu7qMoIAjIiKSrerWhQEDQsC56abQV+eII8IybVrc1cVKAUdERCTb1asH550HCxbAP/8ZRkc+/HA48kh49dW4q4uFAo6IiEiuqFcvTOC5YAGMGQMffgiHHhr66bz2WtzVpZUCjoiISK6pXx8uvDAEnRtvDLOXd+sG3buHKSFqAAUcERGRXNWgQZilfMECuOEGmDULDj44jKUzfXrc1aWUAo6IiEiua9AALr44BJ3rr4d33w2jIh9zDPz3v3FXlxIKOCIiIjVFw4YwcCAsXAjXXRcm9+zaNUwD8dZbcVdXrRRwREREapqGDWHQoBB0Ro+Gt9+GAw6AX/863M8BCjgiIiI1VaNGMHgwLFoE114bmqu6dIFevaCoKO7qqkQBR0REpKZr1AiGDAlBZ9So0AF5//3huONCM1YWUsARERGRoHFjuOyy0HQ1cmQYO6ewEHr3Dh2Ts4gCjoiIiGxs663h8stD0LnqKnjlFdh3X/jtb2HmzLirS4oCjoiIiJStSRO44orQdDViBLz8MnTuDL/7XRhTJ4Mp4IiIiEj5mjSBYcNC0Bk+HKZMgX32gRNOgPffj7u6MingiIiISHKaNoUrrwxBZ9gwmDwZ9t4bTjwxTAeRQTIu4JjZMWb2kZnNM7MhcdcjIiIipTRrFpqsFi6EoUPhhRdC0OnTBz74IO7qgAwLOGZWG7gVOBboAJxsZh3irUpERETK1Lw5XH11OKNz2WXw3HOw117Qt2+YyTxGGRVwgC7APHdf4O4/Ao8AvWOuSURERMrTvHm4rHzRojCezrPPwp57wimnwJw5sZSUaQGnDfB5wuPiaJ2IiIhkuhYt4JprQtPV4MEwcWK4vPx//0t7KZkWcKyMdb7RBmZnm1mRmRUtW7YsTWWJiIhI0lq2DFM/LFwIDz8c+uykWaYFnGKgbcLjAuDLxA3cfZy7F7p7YX5+flqLExERkQrIzw+DA8Yg0wLO28AuZtbezLYC+gITY65JREREskyduAtI5O7rzexc4AWgNnCPu2fG9WYiIiKSNTIq4AC4+3PAc3HXISIiItkr05qoRERERKpMAUdERERyjgKOiIiI5BwFHBEREck5CjgiIiKScxRwREREJOco4IiIiEjOUcARERGRnGPuvuWtMpSZLQM+jbuOLNIS+DruImSz9P1kLn03mU3fT2ZL9fezg7tvMjllVgccqRgzK3L3wrjrkLLp+8lc+m4ym76fzBbX96MmKhEREck5CjgiIiKScxRwapZxcRcg5dL3k7n03WQ2fT+ZLZbvR31wREREJOfoDI6IiIjkHAUcERERyTkKODnOzNqa2ctmNsfMPjCzC+KuSTZlZrXN7F0z+3fctcjGzKypmT1hZnOjf0dd465JfmFmF0X/t802swlmVi/ummoyM7vHzJaa2eyEdc3NbLKZfRLdNktHLQo4uW89cLG77wEcCAwwsw4x1ySbugCYE3cRUqZ/ApPcfXdgH/Q9ZQwzawOcDxS6+55AbaBvvFXVePcBx5RaNwSY4u67AFOixymngJPj3H2xu78T3V9J+M+5TbxVSSIzKwB+DdwVdy2yMTPbGjgUuBvA3X909xXxViWl1AHqm1kdoAHwZcz11Gju/grwTanVvYH7o/v3A8enoxYFnBrEzNoBnYE3461EShkLXAJsiLsQ2cSOwDLg3qgJ8S4zaxh3URK4+xfADcBnwGLgW3d/Md6qpAzbuPtiCH90A63ScVAFnBrCzBoBTwIXuvt3cdcjgZn1Apa6+4y4a5Ey1QH2BW53987AKtJ0el22LOrL0RtoD2wHNDSzU+OtSjKFAk4NYGZ5hHDzkLs/FXc9spGDgePMbBHwCHCkmY2PtyRJUAwUu3vJWc8nCIFHMsPRwEJ3X+bu64CngINirkk2tcTMWgNEt0vTcVAFnBxnZkboPzDH3W+Mux7ZmLtf6u4F7t6O0DnyJXfXX6AZwt2/Aj43s92iVUcBH8ZYkmzsM+BAM2sQ/V93FOoEnokmAv2i+/2AZ9Jx0DrpOIjE6mDgNOB9M5sZrbvM3Z+LsSaRbHIe8JCZbQUsAM6IuR6JuPubZvYE8A7hitF30bQNsTKzCcDhQEszKwaGA6OBx8zsTEIoPTEttWiqBhEREck1aqISERGRnKOAIyIiIjlHAUdERERyjgKOiIiI5BwFHBEREck5CjgiklZmdn40K/dDFdyvnZmdkqq6RCS3KOCISLr9Fejp7n+o4H7tgAoHHDOrXdF9RCT7KeCISNqY2R2ECSwnmtnlZnaPmb0dTWTZO9qmnZm9ambvREvJ0PujgW5mNtPMLjKz083sloTX/reZHR7d/97MrjKzN4GuZrafmU0zsxlm9kLCsPHnm9mHZjbLzB5J52chIqmlgf5EJK2iebcKgb8BH7r7eDNrCrxFmO3egQ3uvsbMdgEmuHthFF4Gunuv6HVOBwrd/dzo8b+BG9x9qpk50MfdH4vmYpsG9Hb3ZWbWB+jh7v3N7EugvbuvNbOm7r4ijR+FiKSQpmoQkbj8ijDR6MDocT1ge+BL4BYz6wT8BOxaidf+iTDBLMBuwJ7A5DBdEbWBxdFzswjTMPwf8H+VeRMikpkUcEQkLgb83t0/2mil2ZXAEmAfQjP6ms3sv56Nm9nrJdxf4+4/JRznA3fvWsZr/Bo4FDgOuMLMOrr7+oq+ERHJPOqDIyJxeQE4L5oFGjPrHK1vAix29w2EiWJLOgmvBBon7L8I6GRmtcysLdBlM8f5CMg3s67RcfLMrKOZ1QLauvvLwCVAU6BRtb07EYmVzuCISFyuBsYCs6KQswjoBdwGPGlmJwIvA6ui7WcB683sPeC+aN+FwPvAbMKM0ptw9x/N7ATgJjNrQvh/byzwMTA+WmfAGPXBEckd6mQsIiIiOUdNVCIiIpJzFHBEREQk5yjgiIiISM5RwBEREZGco4AjIiIiOUcBR0RERHKOAo6IiIjknP8PIRGplEkulAwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "# we use m data points (each data point representing an uncorrupted pixel) \n",
+    "m = 10   \n",
+    "# maximum number of features used \n",
+    "max_r = 10                        \n",
+    "\n",
+    "# read in m data points using max_r features \n",
+    "X, y = GetFeaturesLabels(m, max_r) \n",
+    "  \n",
+    "# vector for storing the training error of LinearRegresion.fit() for each r\n",
+    "linreg_error = np.zeros(max_r)    \n",
+    "\n",
+    "### STUDENT TASK ###\n",
+    "# reg = ...\n",
+    "# reg.xxx(...)\n",
+    "# y_pred = ...\n",
+    "# linreg_error[...] = ... \n",
+    "# Hint: loop \"max_r\" times.\n",
+    "\n",
+    "# YOUR CODE HERE\n",
+    "\n",
+    "for i in range(max_r):\n",
+    "    \n",
+    "    reg = LinearRegression(fit_intercept=False)\n",
+    "    \n",
+    "    reg.fit(X[:,:i+1],y)\n",
+    "    \n",
+    "    y_pred = reg.predict(X[:,:i+1])\n",
+    "    \n",
+    "    linreg_error[i] = mean_squared_error(y, y_pred)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# create a numpy array \"r_values\" containing the values 1,2...,max_r \n",
+    "r_values = np.linspace(1, max_r, max_r, endpoint=True)\n",
+    "# create a plot object which can be accessed using variables \"fig\" and \"axes\"\n",
+    "fig, axes = plt.subplots(1,1, figsize=(8, 5))\n",
+    "# add a curve representing the average squared error for each choice of r \n",
+    "axes.plot(r_values, linreg_error, label='MSE', color='red')\n",
+    "# add captions for the horizontal and vertical axes \n",
+    "axes.set_xlabel('features')\n",
+    "axes.set_ylabel('empirical error')\n",
+    "# add a title to the plot \n",
+    "axes.set_title('Training error vs number of features')\n",
+    "axes.legend()\n",
+    "plt.tight_layout()\n",
+    "# display the plot \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "2f4b53429b1285732d64e6cee8fcd2b4",
+     "grade": false,
+     "grade_id": "cell-797d45ab06df1d04",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "If the task completed correctly, you will see that the training error is decreasing if we use more features to build a linear predictor.\n",
+    "Note that features $x_{3},...x_{10}$ are synthetic random numbers that are in practice completely unrelated with the label $y$ (grayscale value of the center pixel). \n",
+    "\n",
+    "It might seem unintuitive that including these features reduces the error on the training data since we know that these have no real relationship with the label. However, a decreased training error does not necessarily correspond to a better predictive capability on new data. We will explore this issue in more detail in round 4 - for now it is sufficient to be aware of this fact and have faith in that the graph above does not defy common sense."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "38f4fe39d9b45b84f175fd9d2a4c144b",
+     "grade": true,
+     "grade_id": "cell-b0017e97370ec75b",
+     "locked": true,
+     "points": 3,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sanity check tests passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this cell constains visible tests (sanity checks) and \n",
+    "# hidden tests which are used for grading student solutions \n",
+    "\n",
+    "assert linreg_error.shape == (max_r,), \"'linreg_error' has wrong dimensions.\"\n",
+    "assert linreg_error[9] < 0.01 * linreg_error[3], \"training errors not correct\"\n",
+    "assert linreg_error[5] > linreg_error[6], \"training errors not correct\"\n",
+    "print('sanity check tests passed!')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "07c9dc7bfe5a0760de9bfc9abce3516d",
+     "grade": false,
+     "grade_id": "cell-ab5cf2b4d9840b3b",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='varying_features'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "<p><b>Student Task.</b> Varying Number of Data Points.</p>\n",
+    "    \n",
+    "Beside the number of features used to describe data points, another important parameter of a data set is the number $m$ of data points available. We need a sufficent amount of labeled data points to learn a useful predictor. \n",
+    "    \n",
+    "Let us now explore the effect of using a varying number $m$ of data points on the average squared loss achieved by the optimal predictor. The optimal predictor is obtained by minimizing the average squared loss incurred on the $m$ labeled data points. \n",
+    "\n",
+    "- For each choice $m=1,2,\\ldots,10$ read in the first $m$ data points of greyscales using $n=2$ features. This can be done using `GetFeaturesLabels(m,2)`.\n",
+    "- For each $m$, fit a linear model (using `LinearRegression(fit_intercept=False)`) to the first $m$ data points.\n",
+    "- Store the resulting training errors in the numpy array `train_error` of shape (10,). The $m$th entry of this array should be the training error obtained when using $m$ data points. Note that the first entry of a numpy array has index $0$, i.e., the value `train_error[0]` stores the training error obtained for `m=1`. \n",
+    "\n",
+    "<p><b>HINT: In order to avoid mistakes, start looping from 0 (and not from 1)!</b></p>\n",
+    "<p><b>HINT: Don't use `GetFeaturesLabels` again inside the loop.</b></p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "fc02e279e12302a5b99dd27b766e3627",
+     "grade": false,
+     "grade_id": "cell-e57b15d397a6b503",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# maximum number of data points \n",
+    "max_m = 10     \n",
+    "\n",
+    "# read in max_m data points using n=2 features \n",
+    "X, y = GetFeaturesLabels(max_m, 2)   \n",
+    "\n",
+    "# create numpy array \"train_error\" for storing the average squared \n",
+    "# error for each choice of number of data points, i.e., \n",
+    "# train_error[0] is the average error for m=1 labeled data points, \n",
+    "# train_error[1] is the average error for m=2 labeled data points, \n",
+    "# ... \n",
+    "# train_error[max_m-1] is the average error for m=m_max labeled data points\n",
+    "train_error = np.zeros(max_m)         \n",
+    "\n",
+    "### STUDENT TASK ###\n",
+    "# train_error = ... \n",
+    "# Hint: loop \"max_m\" times.\n",
+    "# YOUR CODE HERE\n",
+    "\n",
+    "for i in range(max_m):\n",
+    "    \n",
+    "    reg = LinearRegression(fit_intercept = False)\n",
+    "    reg.fit(X[:i+1,:],y[:i+1,:])\n",
+    "    y_pred = reg.predict(X[:i+1,:])\n",
+    "    train_error[i] = mean_squared_error(y[:i+1,:], y_pred)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# create a numpy array \"m_values\" containing the values 1,2...,max_m\n",
+    "m_values = np.linspace(1, max_m, max_m, endpoint=True)\n",
+    "# create a plot object which can be accessed using variables \"fig\" and \"axes\"\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 4))\n",
+    "# add a curve representing the average squared error for each choice of r\n",
+    "axes.plot(m_values, train_error, label='MSE', color='red')\n",
+    "# add captions for axes of the plot \n",
+    "axes.set_xlabel('number of data points (sample size)')\n",
+    "axes.set_ylabel('training error')\n",
+    "# add title for the plot \n",
+    "axes.set_title('training error vs. number of data points')\n",
+    "axes.legend()\n",
+    "plt.tight_layout()\n",
+    "# display the plot \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "18f5f0c8c799d239ae297c0e9deff40f",
+     "grade": false,
+     "grade_id": "cell-3d10953087fadc50",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "If the task completed correctly, you will see that the training error is increasing if we use more datapoints to fit a linear predictor. Why do you think increasing sample size lead to increase in the training error?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "aac9ea6eea73a1c7e72a9ed00d0bdfbb",
+     "grade": true,
+     "grade_id": "cell-d19e618cf3f2a6e6",
+     "locked": true,
+     "points": 3,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sanity check tests passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this cell constains visible tests (sanity checks) and \n",
+    "# hidden tests which are used for grading student solutions \n",
+    "\n",
+    "assert train_error.shape == (10,), \"'train_error' has wrong dimensions.\"\n",
+    "assert train_error[0] < 100 * train_error[3], \"training errors not correct\"\n",
+    "assert train_error[2] > train_error[1], \"training errors not correct\"\n",
+    "\n",
+    "print('sanity check tests passed!')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "200dd675b3af8a6551404af9a30492a4",
+     "grade": false,
+     "grade_id": "cell-ba2b95035f72f865",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "## Robustness \n",
+    "\n",
+    "An important property of ML methods is their robustness to (small) perturbations in the data. In some applications, few data points might be corrupted (such as the pixels of an aerial photograph) and therefore intrinsically different from all other data points. We prefer ML methods which are affected only little by an error in a few data points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "d94ed7738de9a41f66ab8c6dd6d8383b",
+     "grade": false,
+     "grade_id": "cell-95e180ce6b62fbb2",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='drawplot'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "    \n",
+    "### Demo. Robustness of Linear Regression with Squared Error Loss.\n",
+    "<p>\n",
+    "The code snippet below considers fitting a linear model for greyscales $y$ based on a single feature $x_{1}$. The resulting linear predictor minimizes the average squared error loss on the training data. We then intentionally perturb the first data point by setting $y_{1}^{(1)}$ to an unreasonable value. Using this corrupted data set, we then fit a linear model again and compare the so-obtained linear predictor to the linear predictor obtained previously from the \"clean\" data set.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "e00c98762bfe178452855d5ef75e081f",
+     "grade": false,
+     "grade_id": "cell-5f9aaa714f3351bf",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x864 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "optimal weight w_opt by fitting to (training on) clean training data :  1.172291483907032\n",
+      "optimal weight w_opt by fitting to (training on) perturbed training data :  2.3623678265548533\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "\n",
+    "# read in 10 data points with single feature x_1 and label y \n",
+    "X,y = GetFeaturesLabels(10,1)   \n",
+    "\n",
+    "### fit a linear model to the clean data \n",
+    "reg = linear_model.LinearRegression(fit_intercept=False)\n",
+    "reg = reg.fit(X, y)\n",
+    "y_pred = reg.predict(X)\n",
+    "\n",
+    "# now we intentionally perturb the label of the first data point \n",
+    "y_perturbed = np.copy(y)  \n",
+    "y_perturbed[0] = 1000; \n",
+    "\n",
+    "### fit a linear model to the perturbed data \n",
+    "reg1 = linear_model.LinearRegression(fit_intercept=False)\n",
+    "reg1 = reg1.fit(X, y_perturbed)\n",
+    "y_pred_perturbed = reg1.predict(X)\n",
+    "\n",
+    "# create a plot object which can be accessed using variables \"fig\" and \"axes\"\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(10, 12))\n",
+    "# plot datapoints\n",
+    "axes[0].scatter(X, y, label='data')\n",
+    "# plot linear predictor\n",
+    "axes[0].plot(X, y_pred, color='green', label='Fitted model')\n",
+    "\n",
+    "# now add individual line for each error point\n",
+    "axes[0].plot((X[0], X[0]), (y[0], y_pred[0]), color='red', label='errors') # add label to legend\n",
+    "for i in range(len(X)-1):\n",
+    "    lineXdata = (X[i+1], X[i+1]) # same X\n",
+    "    lineYdata = (y[i+1], y_pred[i+1]) # different Y\n",
+    "    axes[0].plot(lineXdata, lineYdata, color='red')\n",
+    "\n",
+    "# set axes title, labels and legend\n",
+    "axes[0].set_title('fitted model using clean data')\n",
+    "axes[0].set_xlabel('feature x_1')\n",
+    "axes[0].set_ylabel('greyscale y')\n",
+    "axes[0].legend()\n",
+    "\n",
+    "# plot datapoints\n",
+    "axes[1].scatter(X, y_perturbed, label='data')\n",
+    "# plot linear predictor \n",
+    "axes[1].plot(X, y_pred_perturbed, color='green', label='Fitted model')\n",
+    "\n",
+    "# now add individual line for each error point\n",
+    "axes[1].plot((X[0], X[0]), (y_perturbed[0], y_pred_perturbed[0]), color='red', label='errors') # add label to legend\n",
+    "for i in range(len(X)-1):\n",
+    "    lineXdata = (X[i+1], X[i+1]) # same X\n",
+    "    lineYdata = (y_perturbed[i+1], y_pred_perturbed[i+1]) # different Y\n",
+    "    axes[1].plot(lineXdata, lineYdata, color='red')\n",
+    "\n",
+    "# set axes title, labels and legend\n",
+    "axes[1].set_title('fitted model using perturbed training data')\n",
+    "axes[1].set_xlabel('feature x_1')\n",
+    "axes[1].set_ylabel('greyscale y')\n",
+    "axes[1].legend()\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"optimal weight w_opt by fitting to (training on) clean training data : \", reg.coef_[0,0])\n",
+    "print(\"optimal weight w_opt by fitting to (training on) perturbed training data : \", reg1.coef_[0,0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "a70b6520901e9bd9b8c343cf97a810e2",
+     "grade": false,
+     "grade_id": "cell-45ac1f059329f66f",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "By observing the plots above, we can see that the perturbed data point has a very large effect on the optimal predictor. Assuming that the perturbed data point is a true outlier in the sense that it is not from the same distribution as the other points, the inclusion of this data point will result in a predictor that has poor predictive performance on data from the real distribution.\n",
+    "\n",
+    "One solution to this problem is to attempt to remove outliers from the data. Another approach that we will explore next is to use a loss function that is **robust to outliers**, i.e. the trained predictor is less affected by the outliers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "5b9b5b30bdd7abc1fda68d4d592f808d",
+     "grade": false,
+     "grade_id": "cell-2841c0fccc7c99a9",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "## Using Different Loss Function \n",
+    "\n",
+    "The choice of loss function used for a ML methods is a pure design choice. So far, we have focused on the squared error loss since it has convenient computational properties (it can be minimized efficiently, e.g., using gradient descent). However, we will now discuss why it might be a good idea to use a different loss function. \n",
+    "\n",
+    "We observe from the demo above that the resulting linear predictor is heavily affected by corrupting only one single data point. The reason for this sensitivity is rooted in the properties of the squared error loss function used by the class `LinearRegression()`. Indeed by using the loss $(\\hat{y} - y)^{2}$ we force the predictor $\\hat{y}$ to not be too far away from any data point with very large value $y$ of the true label. \n",
+    " \n",
+    "It turns out that using a different loss function to learn a linear predictor can make the learning robust against few outliers. One such robust loss function is known as [\"Huber loss\"](https://en.wikipedia.org/wiki/Huber_loss) $\\mathcal{L}(\\hat{y},y)$. Given a data point with label $y$ and a predicted label $\\hat{y}=h(\\mathbf{x})$ the Huber loss is defined as \n",
+    "$$\\mathcal{L}(y,\\hat{y}) = \\begin{cases} (1/2) (y-\\hat{y})^{2} & \\mbox{ for } |y-\\hat{y}| \\leq   \\varepsilon \\\\ \n",
+    "\\varepsilon(|y-\\hat{y}| - \\varepsilon/2) & \\mbox{ else. }\\end{cases}$$\n",
+    "\n",
+    "Note that the Huber loss contains a parameter $c$ which has to be adapted to the application at hand. \n",
+    "\n",
+    "To learn a linear predictor $h(\\mathbf{x}) = \\mathbf{w}^{T} \\mathbf{x}$ which minimizes the average Huber loss over the labeled data points in the training set we can use the [`HuberRegressor()`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html) class. \n",
+    "\n",
+    "The Huber loss contains two important special cases. The first special case is obtained when $\\varepsilon$ is chosen very large (the precise value depending on the value range of the features and labels) such that the condition $|y-\\hat{y}| \\leq \\varepsilon$ is always satisfied. In this case, the Huber loss becomes the squared error loss $(y-\\hat{y})^{2}$ (with an additional factor 1/2). The second special case is obtained for choosing $\\varepsilon$ very small (close to $0$) such that the condition $|y-\\hat{y}| \\leq \\varepsilon$ is never satisfied. In this case, the Huber loss becomes the absolute loss $|y - \\hat{y}|$ scaled by a factor $\\varepsilon$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "fe95c3d702653e74d419fa11f532a9a8",
+     "grade": false,
+     "grade_id": "cell-616d45f35afde733",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<img src=\"../../../coursedata/R2_Regression/Huber4.jpg\" alt=\"Drawing\" style=\"width: 1000px\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "da791b7c0dad6917f9753e3cf2b5dc8a",
+     "grade": false,
+     "grade_id": "cell-cf09b243fb1ad685",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='drawplot'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "\n",
+    "### Demo. Squared Error and Huber Loss\n",
+    "<p>\n",
+    "The code below plots the squared error loss and the Huber loss for different choices of the parameter $\\varepsilon$. Note that the Huber loss reduces to the squared error loss for a sufficiently large value of the parameter $\\varepsilon$.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "3a92dbdd9ce36abe6c3ce0fb0ff37fd7",
+     "grade": false,
+     "grade_id": "cell-5dfd931359725f65",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Author: Jake VanderPlas\n",
+    "# License: BSD\n",
+    "#   The figure produced by this code is published in the textbook\n",
+    "#   \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n",
+    "#   For more information, see http://astroML.github.com\n",
+    "#   To report a bug or issue, use the following forum:\n",
+    "#    https://groups.google.com/forum/#!forum/astroml-general\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "#------------------------------------------------------------\n",
+    "# Define the Huber loss\n",
+    "def HuberLoss(pred_error, epsilon):\n",
+    "    # pred_error - prediction error y-y_pred\n",
+    "    # epsilon - parameter epsilon 𝜀 \n",
+    "    pred_error = abs(pred_error)\n",
+    "    flag = (pred_error > epsilon)\n",
+    "    return (~flag) * (0.5 * pred_error ** 2) - (flag) * epsilon * (0.5 * epsilon - pred_error)\n",
+    "\n",
+    "#------------------------------------------------------------\n",
+    "# Plot for several values of epsilon\n",
+    "fig = plt.figure(figsize=(10, 5)) # set figure size\n",
+    "ax = fig.add_subplot(111) # add 1 subplot\n",
+    "\n",
+    "pred_error = np.linspace(-5, 5, 100) # create linear space from -5 to 5 with 100 steps\n",
+    "\n",
+    "for epsilon in (1, 2, 10): # loop through values 1, 2, 10\n",
+    "    loss = HuberLoss(pred_error, epsilon)\n",
+    "    ax.plot(pred_error, loss, '-k') # plot x and y\n",
+    "\n",
+    "    if epsilon > 10:\n",
+    "        s = r'\\infty' # set s to infinity sign (string format)\n",
+    "    else:\n",
+    "        s = str(epsilon) # set s to string of number epsilon\n",
+    "\n",
+    "    ax.text(pred_error[6], loss[6], '$\\epsilon=%s$' % s,\n",
+    "            ha='center', va='center',\n",
+    "            bbox=dict(boxstyle='round', ec='k', fc='w')) # add test to each line\n",
+    "\n",
+    "ax.plot(pred_error, np.square(pred_error),label=\"squared loss\") # plot the sqared loss (blue line)\n",
+    "\n",
+    "ax.set_xlabel(r'$y - \\hat{y}$') # set x labels\n",
+    "ax.set_ylabel(r'loss $\\mathcal{L}(y,\\hat{y})$') # set y label\n",
+    "ax.legend() # show legend in plot\n",
+    "plt.show() # show the plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "9104c216a9579ba70b927aaefd34989d",
+     "grade": false,
+     "grade_id": "cell-f0a819809dd992a3",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "Assume we have a dataset that could be perfectly fitted to a linear function with the exception of one outlier data point. Let's take a look at two linear predictors. The first linear predicturs uses some weight vector $\\mathbf{w}^{(1)}$ and the other predictor using a different weight vector $\\mathbf{w}^{(2)}$. \n",
+    "\n",
+    "The weight vector $\\mathbf{w}^{(1)}$ is chosen such that $h(\\mathbf{x}) = \\big(\\mathbf{w}^{(1)} \\big)^{T} \\mathbf{x}$ perfectly fits the data points which are not outliers (see top-left plot below). \n",
+    "\n",
+    "The weight vector $\\mathbf{w}^{(2)}$ is chosen by minimizing the avaerage squared error loss over ALL data points (including the outlier). \n",
+    "\n",
+    "We see that one data point is outlier and model 1 is an optimal predictor for the dataset. Let's see how differents loss functions estimate the quality of these predictors. On the right side absolute, square and huber loss for each datapoint is plotted and total loss indicated in the subplots legends. What can you say about robustness of these loss functions to the outlier?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "61484ebd11ee9536331637e777968373",
+     "grade": false,
+     "grade_id": "cell-882a57eba6fe978c",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<img src=\"../../../coursedata/R2_Regression/Huber.png\" alt=\"Drawing\" style=\"width:1000px\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "b5203329eeb7c2048bf32a7810a210f3",
+     "grade": false,
+     "grade_id": "cell-87c2d62347509737",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='drawplot'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "    \n",
+    "### Demo. Robustness of Linear Regression with Huber Loss.\n",
+    "\n",
+    "<p>The code snippet below fits a linear model for greyscales $y$ based on a single feature $x_{1}$ using the Huber loss. We also intentionally perturb the first data point by setting $y_{1}^{(1)}$ to an unreasonable value. Using this corrupted data set, we fit a linear model (under Huber loss) again and compare the so-obtained linear predictor to the linear predictor obtained from the \"clean\" data set.</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "67286a689720485251e6542e6d2e8268",
+     "grade": false,
+     "grade_id": "cell-53fc0a75788a46a8",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x864 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "optimal weight w_opt by fitting on clean data :  1.0890434272688132\n",
+      "optimal weight w_opt by fitting on perturbed data :  1.0804854338400913\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "from sklearn.linear_model import HuberRegressor\n",
+    "\n",
+    "X,y = GetFeaturesLabels(10,1)   # read in 10 data points with single feature x_1 and label y \n",
+    "\n",
+    "# regressor gives a warning if this isn't done, changes y.shape from (10, 1) to (10,)\n",
+    "y = y.ravel()\n",
+    "\n",
+    "### fit a linear model (using Huber loss) to the clean data \n",
+    "reg = HuberRegressor().fit(X, y)\n",
+    "y_pred = reg.predict(X)\n",
+    "\n",
+    "# now we intentionaly perturb the label of the first data point \n",
+    "y_perturbed = np.copy(y)  \n",
+    "y_perturbed[0] = 1000; \n",
+    "\n",
+    "### fit a linear model (using Huber loss) to the perturbed data \n",
+    "reg1 = HuberRegressor().fit(X, y_perturbed)\n",
+    "y_pred_perturbed = reg1.predict(X)\n",
+    "\n",
+    "# create a plot object which can be accessed using variables \"fig\" and \"axes\"\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(10,12))\n",
+    "# plot datapoints\n",
+    "axes[0].scatter(X, y, label='data')\n",
+    "# plot linear predictor\n",
+    "axes[0].plot(X, y_pred, color='green', label='Fitted model')\n",
+    "\n",
+    "# now add individual line for each error point\n",
+    "axes[0].plot((X[0], X[0]), (y[0], y_pred[0]), color='red', label='errors') # add label to legend\n",
+    "for i in range(len(X)-1):\n",
+    "    lineXdata = (X[i+1], X[i+1]) # same X\n",
+    "    lineYdata = (y[i+1], y_pred[i+1]) # different Y\n",
+    "    axes[0].plot(lineXdata, lineYdata, color='red')\n",
+    "\n",
+    "# set axes title, labels and legend\n",
+    "axes[0].set_title('fitted model using clean data')\n",
+    "axes[0].set_xlabel('feature x_1')\n",
+    "axes[0].set_ylabel('greyscale y')\n",
+    "axes[0].legend()\n",
+    "\n",
+    "axes[1].scatter(X, y_perturbed, label='data')\n",
+    "# plot linear predictor\n",
+    "axes[1].plot(X, y_pred_perturbed, color='green', label='Fitted model')\n",
+    "\n",
+    "# now add individual line for each error point\n",
+    "axes[1].plot((X[0], X[0]), (y_perturbed[0], y_pred_perturbed[0]), color='red', label='errors') # add label to legend\n",
+    "for i in range(len(X)-1):\n",
+    "    lineXdata = (X[i+1], X[i+1]) # same X\n",
+    "    lineYdata = (y_perturbed[i+1], y_pred_perturbed[i+1]) # different Y\n",
+    "    axes[1].plot(lineXdata, lineYdata, color='red')\n",
+    "\n",
+    "# set axes title, labels and legend\n",
+    "axes[1].set_title('fitted model using perturbed data')\n",
+    "axes[1].set_xlabel('feature x_1')\n",
+    "axes[1].set_ylabel('greyscale y')\n",
+    "axes[1].legend()\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"optimal weight w_opt by fitting on clean data : \", reg.coef_[0])\n",
+    "print(\"optimal weight w_opt by fitting on perturbed data : \", reg1.coef_[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "ec63e8c944a083f609fd637b4f9d6d1b",
+     "grade": false,
+     "grade_id": "cell-c77f887d9194b713",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "We can see that the predictors trained on the original and perturbed data are very similar when using the Huber loss, in contrast to the large difference in the predictors that minimize the mean-squared error."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "418442f97be7823ff699810611c7f86d",
+     "grade": false,
+     "grade_id": "cell-c0992d0c8d156b25",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='varying_features'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "    \n",
+    "### Demo. Varying Number of Features with Huber Loss. \n",
+    "    \n",
+    "In principle you can choose how many of the available features $x_{1}, x_{2},\\ldots,$ \n",
+    "of a pixel you want to use to in order to predict the greyscale $y$. Let us now explore \n",
+    "the effect of using a varying number $r$ of features on the resulting error and \n",
+    "computational complexity (runtime). <br />\n",
+    "\n",
+    "In particular, for each $r=1,2,\\ldots,10$, the code snippet below fits a linear model under \n",
+    "Huber loss to the pixels dataset (using $m=10$ data points) by using only the \n",
+    "first $r$ features $x_{1},...,x_{r}$ of a pixel. \n",
+    "<br />    \n",
+    "- The first $r$ features and labels (greyscale) for the pixels can be obtained using `GetFeaturesLabels(m,r)`.<br />\n",
+    "- For each value of $r$, the resulting training error (using the Python function `mean_squared_error()`) of the fitted linear model is calculated. <br />\n",
+    "- The results are stored in the vector `linreg_error`. \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "2d7c1808bcb1be23de0f827ad00a8e82",
+     "grade": false,
+     "grade_id": "cell-e77bf5b72cac619d",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "\n",
+    "# we use 10 data points (each data point representing an uncorrupted pixel)\n",
+    "m = 10   \n",
+    "\n",
+    "# maximum number of features used to characterize a data point\n",
+    "max_r = 10                        \n",
+    "\n",
+    "# read in m data points each described by max_r features\n",
+    "X,y = GetFeaturesLabels(m, max_r)  \n",
+    "\n",
+    "# scale the features\n",
+    "X = preprocessing.scale(X)\n",
+    "\n",
+    "# vector for storing the training error of LinearRegresion.fit() for each r\n",
+    "linreg_error = np.zeros(max_r)    \n",
+    "\n",
+    "for r in range(max_r):\n",
+    "    # create an object \"reg_hub\" which represents a linear hypothesis space \n",
+    "    # consisting of predictor maps h(x) = w^{T} x  (without an intercept term or \"offset\")\n",
+    "    # this object uses the Huber loss function for finding the best weight vector \n",
+    "    reg_hub = HuberRegressor(fit_intercept=False) \n",
+    "    # find the best weight vector w by minimizing the average Huber loss \n",
+    "    # the resulting optimal weight vector is stored in \"reg_hub.coef_\" \n",
+    "    reg_hub = reg_hub.fit(X[:,:(r+1)], y.ravel())\n",
+    "    # apply the optimal linear predictor (using optimal weight vector) to the \n",
+    "    # data points whose feature vectors are stored in numpy array \"X\"\n",
+    "    pred = reg_hub.predict(X[:,:(r+1)])\n",
+    "    # computer the resulting average squared loss incurred by the optimal predictor \n",
+    "    linreg_error[r] = mean_squared_error(y, pred)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 5))\n",
+    "ax = fig.add_subplot(111)\n",
+    "\n",
+    "# create a numpy array \"r_vals\" containing the values 1,2...,max_r\n",
+    "r_vals = np.linspace(1, max_r, max_r, endpoint=True)\n",
+    "\n",
+    "# add curve depicting the resulting training error for each choice of number r of features \n",
+    "ax.plot(r_vals, linreg_error, label='MSE', color='red')\n",
+    "\n",
+    "# add captions to plot \n",
+    "ax.set_xlabel('features')\n",
+    "ax.set_ylabel('empirical error')\n",
+    "ax.set_title('training error vs number of features')\n",
+    "ax.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "9af438139a82cb3fcf7ca04924e274d8",
+     "grade": false,
+     "grade_id": "cell-599ce61a3486169c",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<a id='varying_features'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "<p><b>Student Task</b> Choosing the optimal epsilon for HuberRegressor</p>\n",
+    "    \n",
+    "\n",
+    "\n",
+    "The Huber loss depends on a tuning-parameter $\\varepsilon$ which should be adapted to the application at hand. For example, if we want to use the value $\\varepsilon=1.1$ we need to use the Python command \n",
+    "\n",
+    "`reg = HuberRegressor(epsilon = 1.1).fit(X,y)` \n",
+    "\n",
+    "to determine the optimal predictor which minimizes the Huber loss with $\\varepsilon=1.1$. \n",
+    "\n",
+    "One simple approach to implement this is to try out different values for $\\varepsilon$. For each value of $\\varepsilon$, we compute a linear predictor by minimizing the average Huber loss for that choice of $\\varepsilon$. We then compare the resulting training errors achieved by these (typically different) predictors that are obtained for different choices of $\\varepsilon$. \n",
+    "\n",
+    "- read in $m=10$ data points, each data point characterized by $n=1$ feature and a numeric label \n",
+    "- store the features of the data points in the numpy array `X`of shape (10,1) \n",
+    "- store the labels of the data points in the numpy array `y`of shape (10,1)  \n",
+    "- create anoter numpy array `y_perturbed` which is identifcal with `y` except for the first entry which is set to `y_perturbed[0]=10000`\n",
+    "- determine the weight vectors $\\mathbf{w}^{(\\varepsilon)}$ for a linear predictor by minimizing the average Huber loss for the choices $\\varepsilon=1,1.2,1.4,1.6,1.8,2$ on the perturbed data points (using features `X` and labels `y_perturbed`)\n",
+    "- for each of the resulting linear predictors $h(\\mathbf{x}) = \\big( \\mathbf{w}^{(\\varepsilon)} \\big)^{T} \\mathbf{x}$, determine the average squared error loss incurred on the $m-1$ labeled data points (use the function `mean_squared_error()` for this) whose features are stored in `X[1:]` and labels in `y[1:]`. Thus, we evaluate the predictor on the $m-1$ unperturbed data points. \n",
+    "- store the resulting average squared error loss in the numpy array `err_vs_epsilon` of shape (6,1)\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "00d1485f21187cba2b22a124aa3be744",
+     "grade": false,
+     "grade_id": "cell-3f265672d8e972fa",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Errors:\n",
+      "[184.53685192 183.73532108 179.88179686 175.60651004 166.84675307\n",
+      " 171.05664397]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "from sklearn.linear_model import HuberRegressor\n",
+    "\n",
+    "# read in 10 data points with single feature x_1 and label y \n",
+    "X,y = GetFeaturesLabels(10,1)  \n",
+    "\n",
+    "# create a numpy array with the values for the paramter c \n",
+    "epsilon_vals = [1,1.2,1.4,1.6,1.8,2]\n",
+    "\n",
+    "# create a numpy array \"y_perturbed\" by copying the values of the \n",
+    "# numpy array \"y\" which contains the label values of the data points \n",
+    "y_perturbed = np.copy(y)  \n",
+    "\n",
+    "# now we intentionaly perturb the label of the first data point \n",
+    "# by setting it to 10000\n",
+    "y_perturbed[0] = 10000; \n",
+    "\n",
+    "### STUDENT TASK ###\n",
+    "# YOUR CODE HERE\n",
+    "\n",
+    "err_vs_epsilon = np.zeros(6)\n",
+    "\n",
+    "for i, e in enumerate(epsilon_vals):\n",
+    "    \n",
+    "    reg = HuberRegressor(epsilon = e).fit(X,y_perturbed)\n",
+    "    y_pred = reg.predict(X)\n",
+    "    err_vs_epsilon[i] = mean_squared_error(y, y_pred)\n",
+    "\n",
+    "\n",
+    "# Print errors\n",
+    "print('Errors:')\n",
+    "print(err_vs_epsilon)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "83460dbe60fe122711b6dc92387528d3",
+     "grade": true,
+     "grade_id": "cell-6a7a343d9aa2cba5",
+     "locked": true,
+     "points": 3,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity check tests passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this cell contains visible tests (sanity checks) and \n",
+    "# hidden tests which are used for grading student solutions \n",
+    "\n",
+    "assert err_vs_epsilon.shape[0] == 6, \"'linreg_error' has wrong dimensions.\"\n",
+    "assert err_vs_epsilon[2] < err_vs_epsilon[1], \"training errors not correct\"\n",
+    "assert err_vs_epsilon[1] < err_vs_epsilon[0], \"training errors not correct\"\n",
+    "\n",
+    "print('Sanity check tests passed!')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "8c1da9852330a8995009bff93a3bda8f",
+     "grade": false,
+     "grade_id": "cell-4e16d03fab72ba2c",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<a id='Bonus Huber'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <b>Bonus Task.</b> Huber loss. \n",
+    "    \n",
+    "Bonus task worth of 50 points.\n",
+    "    \n",
+    "Explain the meaning of parameter \"epsilon\" in HuberRegressor. In particular, how would you choose the value \"epsilon\" for the Huber loss based on some prior knowledge about the (properties of the) outliers? \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "82516b3393fb1738b5a2e67864944bf6",
+     "grade": false,
+     "grade_id": "cell-a62472a3ab8f38a5",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "## Take Home Quiz\n",
+    "\n",
+    "Try to answer the following questions by setting the `answer_R2_Q??` \n",
+    "variable for each question to the number of the correct answer. E.g. if you think that the second answer in the first quiz question is the right one, then set `answer_R2_Q1=2`. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "178dda76fd5e031b60ad402950295d91",
+     "grade": false,
+     "grade_id": "cell-e611768d73f86fa7",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='QuestionR2_1'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <p><b>Student Task.</b> Question R2.1. </p>\n",
+    "\n",
+    " <p>When is a machine learning problem called a regression problem ?</p>\n",
+    "\n",
+    "<ol>\n",
+    "  <li> When the quantity of interest (the label) is a numeric quantity. </li>\n",
+    "  <li> When the data is stored in a spreadsheet.</li>\n",
+    "  <li> When the method uses audio data.  </li>\n",
+    "  <li> When the quantity of interest takes on only a finite number of different values (e.g. \"-1\",\"0\" or \"4\").\n",
+    "</ol> \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "432b0e4e14c5872cbf513a0666917573",
+     "grade": false,
+     "grade_id": "cell-9d35a0850eda0752",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# answer_Q1\n",
+    "\n",
+    "# answer_R2_Q1  = ...\n",
+    "# YOUR CODE HERE\n",
+    "\n",
+    "answer_R2_Q1  = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "9c77347da957ce02c96118b7d754f758",
+     "grade": true,
+     "grade_id": "cell-43ad391c8e33387a",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity check tests passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this cell constains visible tests (sanity checks) and \n",
+    "# hidden tests which are used for grading student solutions \n",
+    "\n",
+    "assert answer_R2_Q1 in [1, 2, 3, 4], '\"answer_R2_Q1\" Value should be an integer between 1 and 4.'\n",
+    "print('Sanity check tests passed!')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "a73aca467d6ed65b8b86d51c7be4a864",
+     "grade": false,
+     "grade_id": "cell-f98434b12dacd59b",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='QuestionR2_2'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <p><b>Student Task.</b> Question R2.2.</p>\n",
+    "    <p> What is the effect of using more features for learning (fitting) a linear predictor via minimizing the average squared error on training data?</p>\n",
+    "    <ol>\n",
+    "      <li> The training error typically increases. </li>\n",
+    "      <li> The training error typcially decreases. </li>\n",
+    "      <li> The training error does not depend on the number of features. </li>\n",
+    "    </ol> \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "3ee88371058548003809a8ff87edd9c3",
+     "grade": false,
+     "grade_id": "cell-c6d81be6a121716c",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# answer_Q2\n",
+    "\n",
+    "# answer_R2_Q2  = ...\n",
+    "# YOUR CODE HERE\n",
+    "answer_R2_Q2  = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "c63bb51fdbc9ba5fc37733593d426d18",
+     "grade": true,
+     "grade_id": "cell-a0269c3b1e477408",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity check tests passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test cell, please don't remove.\n",
+    "\n",
+    "assert answer_R2_Q2 in [1, 2, 3], '\"answer_R2_Q2\" Value should be an integer between 1 and 3.'\n",
+    "print('Sanity check tests passed!')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "98f6da8bae00c7a31f3bf3927e0762bb",
+     "grade": false,
+     "grade_id": "cell-02646ec2841586e2",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='QuestionR2_3'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <p><b>Student Task.</b> Question R2.3.</p>\n",
+    "    <p> What is the effect of adding more data points to the training data when using linear predictors and the squared error loss?</p>\n",
+    "    <ol>\n",
+    "      <li> The training error always decreases when adding more data points to the training set.  </li>\n",
+    "      <li> The training error might increase when adding more data points to the training set.  </li>\n",
+    "    </ol> \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "967d9e4505267ec75d4ef267f58f8b4e",
+     "grade": false,
+     "grade_id": "cell-1f5ce374962033e7",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# answer_Q3\n",
+    "\n",
+    "# answer_R2_Q3  = ...\n",
+    "# YOUR CODE HERE\n",
+    "answer_R2_Q3  = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "83a3043ae1e3ae2bea848a0c07fd7baa",
+     "grade": true,
+     "grade_id": "cell-1bf8dff2ceba3115",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity check tests passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this cell constains visible tests (sanity checks) and \n",
+    "# hidden tests which are used for grading student solutions \n",
+    "\n",
+    "assert answer_R2_Q3 in [1, 2], '\"answer_R2_Q3\" Value should be an integer between 1 and 2.'\n",
+    "print('Sanity check tests passed!')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "55ba452c91c107de5bfc86728da3f941",
+     "grade": false,
+     "grade_id": "cell-5c750f08c418572f",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='QuestionR2_4'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <p><b>Student Task.</b> Question R2.4.</p>\n",
+    "    <p> How does the resulting regression method differ when using either squared error or Huber loss?</p>\n",
+    "    <ol>\n",
+    "      <li> Using Huber loss makes the resulting method more robust against outliers, i.e., the learned predictor does not vary too much if a few training data points are perturbed.  </li>\n",
+    "      <li> Using squared error loss makes the resulting method more robust against outliers.  </li>\n",
+    "    </ol> \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "3eb65454ab2eb8a60de77c213a88bde5",
+     "grade": false,
+     "grade_id": "cell-f1395b9db09b190d",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# answer_Q4\n",
+    "\n",
+    "# answer_R2_Q4  = ...\n",
+    "# YOUR CODE HERE\n",
+    "answer_R2_Q4  = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "47deb7827e08d8801a60cc4ad79e9341",
+     "grade": true,
+     "grade_id": "cell-f3f22d98a84a2e74",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity check tests passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this cell constains visible tests (sanity checks) and \n",
+    "# hidden tests which are used for grading student solutions \n",
+    "\n",
+    "assert answer_R2_Q4 in [1, 2, 3], '\"answer_R2_Q4\" Value should be an integer between 1 and 3.'\n",
+    "print('Sanity check tests passed!')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "173.95px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
GitLab