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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
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+     "cell_type": "markdown",
+     "checksum": "34de0efc9ddba3293e448b0f08b02db3",
+     "grade": false,
+     "grade_id": "Introduction",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "# Round 1 - Components of Machine Learning\n",
+    "\n",
+    "<img src=\"../../../coursedata/R1_ComponentsML/AMLProblem.png\" alt=\"Drawing\" style=\"width: 600px;\"/>\n",
+    "\n",
+    "Many machine learning (ML) problems and methods consist of three components: \n",
+    "\n",
+    "1. Data points as the basic (atomic) unit of information. Data points are characterized by features, which are  properties that can be measured (or computed) easily. Besides features, data points are often associated with certain labels that represent some higher-level information or quantity of interest. In contrast to features, labels are difficult to acquire and much of machine learning is about to develop methods that allow to estimate or predict the labels of a data point based on its features.  \n",
+    "\n",
+    "2. A hypothesis space (also referred to as a ML model) consisting of computationally feasible predictor functions.\n",
+    "\n",
+    "3. A loss function that is used to assess the quality of a particular predictor function. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
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+     "grade_id": "cell-ccfc045530551950",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "## Learning Goals\n",
+    "\n",
+    "* Learn to make useful definitions for what data points (examples, samples), features and labels are in different real-life applications. \n",
+    "* Learn how to represent data as numpy arrays which are, in turn, the Python implemenation of vectors and matrices.   \n",
+    "* Learn to use (\"toy\") datasets provided by the Python library `scikit-learn`. \n",
+    "* Learn about the concept of hypothesis spaces. \n",
+    "* Learn how to fit (linear) predictions functions to data. \n",
+    "\n",
+    "This notebook contains several student tasks which require you to write a few lines of Python code to solve small problems. In particular, you have to fill in the gaps marked as **Student Task**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
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+   },
+   "source": [
+    "<b><center><font size=4>Additional material</font></center></b>\n",
+    "\n",
+    "<b><font size=4>Videos</font></b>\n",
+    "\n",
+    "* [Data](https://youtu.be/WWYRH3x7_5M), [Hypothesis Space](https://youtu.be/CDcRfak1Mh4), [Hypothesis Space of Linear Models](https://youtu.be/Mch5hmhVuiA), [Hypothesis Space of Decision Trees](https://youtu.be/0FmaLfjAaRE), [Hypothesis Space of Deep Learning](https://youtu.be/im8mweIrpAM),[Loss Functions](https://www.youtube.com/watch?v=Uv9lihDfsBs&t=4s)\n",
+    "\n",
+    "<b><font size=4>Tutorials</font></b>\n",
+    "\n",
+    "* components of ML can be found under [this link](https://arxiv.org/pdf/1910.12387.pdf) \n",
+    "\n",
+    "* Python library `numpy` can be found under [this link](https://hackernoon.com/introduction-to-numpy-1-an-absolute-beginners-guide-to-machine-learning-and-data-science-5d87f13f0d51).\n",
+    "\n",
+    "* \"Learn the Basics\" and \"Data Science Tutorial\" sections from [this link](https://www.learnpython.org/en/).\n",
+    "\n",
+    "* a quick refresher for basic properties of matrices can be found under [this link](http://math.mit.edu/~gs/linearalgebra/linearalgebra5_1-3.pdf)\n",
+    "and [this link](https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780470549094.app1)\n",
+    "\n",
+    "* mathematical notation [this link](https://en.wikipedia.org/wiki/List_of_mathematical_symbols)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "4dedad0e8e48117b4f2bfbe79b8e45db",
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+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "## Data as Matrices and Vectors\n",
+    "<a id=\"Q1\"></a>\n",
+    "\n",
+    "To implement ML methods, we need to be able to efficiently **store and manipulate** data.  A quite powerful tool to represent and manipulate data are [vectors and matrices](https://en.wikipedia.org/wiki/Matrix_(mathematics)) which are, in turn, special cases of [tensors](https://en.wikipedia.org/wiki/Tensor). \n",
+    "\n",
+    "The data points arising in many application domains can often be characterized by a list of numeric attributes. This numeric attributes or \"features\", $x_{r}$ can be stacked conveniently into a vector $\\mathbf{x}=\\big(x_{1},\\ldots,x_{n}\\big)^{T}$. Many ML methods, such as linear regression (see round 2) or logistic regression (see round 3), use predictor functions of the form $h(\\mathbf{x}) = \\mathbf{w}^{T} \\mathbf{x}$ with some weight vector $\\mathbf{w}$. \n",
+    "\n",
+    "Once we restrict ourselves to linear functions of the form $h(\\mathbf{x}) = \\mathbf{w}^{T} \\mathbf{x}$, we can represent a predictor function by the weight vector $\\mathbf{w}$. Indeed, given the weight vector $\\mathbf{w}$, we can evaluate the predictor function for any feature vector $\\mathbf{x}$ as $h(\\mathbf{x}) = \\mathbf{w}^{T} \\mathbf{x}$. Thus, not noly we can represent data using a vector, but also the predictor functions applied to this data. \n",
+    "\n",
+    "Assume we have a set of data points which we index with $i=1,...,m$. The $i$th data point is characterized by the feature vector $\\mathbf{x}^{(i)} = \\big( x_{1}^{(i)}, \\ldots, x^{(i)}_{n} \\big)^{T}$ \n",
+    "Accepted way to organize the data in ML is following: features are stored in the (\"feature\") matrix **X** with each row containing the data for each data point ($m$ - number of data points) and with each column storing the data of each feature vector ($n$ - number of features):\n",
+    "\n",
+    "\\begin{equation}\n",
+    "\\mathbf{X}  = \\begin{pmatrix} X_{1,1} & X_{1,2}& \\ldots & X_{1,n} \\\\ \n",
+    "X_{2,1} & X_{2,2}& \\ldots & X_{2,n} \\\\ \n",
+    "\\vdots & \\vdots & \\vdots & \\vdots \\\\ \n",
+    "X_{m,1} & X_{m,2} & \\ldots & X_{m,n} \\end{pmatrix}\\in \\mathbb{R}^{m \\times n}   \\quad \\quad (Eq.1)\n",
+    "\\end{equation} \n",
+    "\\\n",
+    "The matrix $\\mathbf{X} \\in \\mathbb{R}^{m \\times n}$ is stored in Python as a numpy array of shape (m,n). The $i$th row of the matrix $\\mathbf{X}$ is the feature vector $\\mathbf{x}^{(i)}$ of the $i$th data point. \n",
+    "\n",
+    "Labels of data points are stored in vector **y**: \n",
+    "\n",
+    "\\begin{equation}\n",
+    "\\mathbf{y}  = \\begin{pmatrix} y_{1} \\\\ y_{2} \\\\ \\vdots \\\\ y_{m} \\end{pmatrix}\\in \\mathbb{R}^{m}\n",
+    "\\end{equation} \n",
+    "\\\n",
+    "**y** vector is represented as a numpy array of shape (m,1)\n",
+    "\\\n",
+    "\\\n",
+    "\\\n",
+    "$m$ - number of data points\\\n",
+    "$n$ - number of features\\\n",
+    "$\\mathbf{X}$       - upper-case bold letters denote a matrix  \\\n",
+    "$\\mathbf{x}$       - lower-case bold letters denote a vector  \\\n",
+    "$\\mathbf{x}^{T}$   - transpose of vector x \\\n",
+    "$x_{1}$            - first entry of vector x\\\n",
+    "$x_{r}$            - $r$th entry of vector x\\\n",
+    "$\\mathbf{x}^{(i)}$ - feature vector of $i$th data point\\\n",
+    "$x_{r}^{(i)}$      - $r$th feature of $i$th data point\\\n",
+    "$\\mathbb{R}$       - real numbers\\\n",
+    "$\\mathbb{R}^{n}$   - [real coordinate space](https://en.wikipedia.org/wiki/Real_coordinate_space) consisting of length-$n$ lists of real numbers \\\n",
+    "$\\mathbb{R}^{m \\times n}$ - matrices with $m$ rows and $n$ columns of real-valued numbers$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Features and Labels "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us illustrate the main ML terminology using a concrete example. Imagine that we want to build a model for classifying songs according to their genre (such as \"Pop\", \"Blues\" or \"Hip-Hop\"). In this case the **data points** will be songs, one particular song correspond to one particular data point. To build a classifier for the song genre, we need some labeled data points, i.e., songs for which we know the correct genre. Each data point has several   **features**, which characterize the songs. Features include e.g., the city where the song was produced, the length of the song's lyrics, its tempo or even the power spectrum of audio signal. The quantity of interest or **label** in this case is the genre to which the song belongs to. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"../../../coursedata/R1_ComponentsML/FeaturesLabels.jpg\" alt=\"Drawing\" style=\"width: 1000px;\"/>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "d39bc00c25a6bf24722237c1731252dc",
+     "grade": false,
+     "grade_id": "cell-5286954cef704fd2",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<a id='Bonus1'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <b>Bonus Task.</b> Machine Learning in your life. \n",
+    "    \n",
+    "Bonus task worth of 50 points.\n",
+    "    \n",
+    "Produces a short video/slides/description where some real-life situation is modelled as a machine learning problem. \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "f17f6ceca1b11bd1bc9f4756f75b275c",
+     "grade": false,
+     "grade_id": "cell-f22c842fc8d33ae5",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "## Scikit-Learn Data\n",
+    "\n",
+    "The Python library `scikit-learn` comes with a few standard datasets, for instance the [iris](https://scikit-learn.org/stable/datasets/index.html#iris-plants-dataset) and [digits](https://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) datasets for classification and the [boston house prices](https://scikit-learn.org/stable/datasets/index.html#boston-house-prices-dataset) and [linnerrud](https://scikit-learn.org/stable/datasets/index.html#linnerrud-dataset) datasets for regression.\n",
+    "These are [Toy datasets](https://scikit-learn.org/stable/datasets/index.html#toy-datasets) - small datasets that do not require to download any file from some external websites. However, `sciki-learn` also provides significantly larger datasets that are referred to as [Real world datasets](https://scikit-learn.org/stable/datasets/index.html#real-world-datasets) which can be accessed online. \n",
+    "\n",
+    "Find more information about `scikit-learn` datasets here: https://scikit-learn.org/stable/datasets/index.html\n",
+    "\n",
+    "More datasets can be found here:\n",
+    "https://archive.ics.uci.edu/ml/index.php\n",
+    "https://www.kaggle.com/datasets\n",
+    "\n",
+    "Let us now take a closer look on some of these `scikit-learn` datasets and try to identify features and labels for these datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "dd606ff289bb7817d7a55491233d7c18",
+     "grade": false,
+     "grade_id": "cell-33131fc4ff3f917e",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "### Toy datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "3590a8a508bcafadfe2c282e15fd362a",
+     "grade": false,
+     "grade_id": "cell-12d9b73f716ec849",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "The code snippet below shows how to download datasets from `sklearn` and how to access features and labels of the data points in these datasets. Small toy datasets are imported using command `from sklearn import datasets`. \n",
+    "\n",
+    "These datasets are stored using the [`bunch` data type](https://pypi.org/project/bunch/), which is similar to the `dictionary` data type. A `bunch` object containes key-value pairs. Most datasets contain at least the keys `'data', 'target', 'target_names','DESCR'`. The value of the key `DESCR` is a short description of the dataset. The value of the `'target_names'` and `'target'` keys are the labels' names and labels, respectively, for each data point. \n",
+    "By default, the labels of data points are always numbers. \n",
+    "\n",
+    "In a classification problem, these numbers are integers starting from $0$. The values of the key ``target_names`` provide a textual description of the meaning of different label values. E.g., the labels of images could be $y=0$ or $y=1$ and the label names would be 0=\"Cat\", 1=\"Dog\". \n",
+    "The value of the `'data'`key is the feature matrix (see <a href='#Q1'>(Eq.1)</a>). \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "160bc7d76742342ed12d06ecb82aa6ed",
+     "grade": false,
+     "grade_id": "cell-3c309834973bfbfe",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<b><center><font size=3>Explore the dataset</font></center></b>\n",
+    "The \n",
+    "**[\"Digits\" dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)** contains images of hand-written digits. This dataset can be used for testing a classification method to [recognize digits from hand-written images](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#recognizing-hand-written-digits).\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "3144c25576c9cf1501007a2718a47adc",
+     "grade": false,
+     "grade_id": "cell-8ddb11cc7ec86ae8",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# import toy datasets from sklearn library\n",
+    "from sklearn import datasets \n",
+    "\n",
+    "# load the digits dataset into the bunch object \"digits\"\n",
+    "digits = datasets.load_digits() \n",
+    "# print the keys of all (key,value) pairs contained in digits\n",
+    "digits.keys() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "5cac1923cf3b1a7f94d8a8485e465d02",
+     "grade": false,
+     "grade_id": "cell-d1283347e1eeb490",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _digits_dataset:\n",
+      "\n",
+      "Optical recognition of handwritten digits dataset\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      "    :Number of Instances: 5620\n",
+      "    :Number of Attributes: 64\n",
+      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
+      "    :Missing Attribute Values: None\n",
+      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
+      "    :Date: July; 1998\n",
+      "\n",
+      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
+      "https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
+      "\n",
+      "The data set contains images of hand-written digits: 10 classes where\n",
+      "each class refers to a digit.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(digits.DESCR[:660]) # print out a short description of the dataset (only first 660 characters are used)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "47e5c43eb0d77c840a86a4071471ab7b",
+     "grade": false,
+     "grade_id": "cell-70ffe5a53ae5b399",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "We can see from the description of the dataset that the **data points** are characterized by 8x8 pixel images of hand-written digits. Each pixel of an image is represented by an integer $0,\\ldots,16$ with $0$ meaning black pixel and $16$ meaning white pixel. \n",
+    "\n",
+    "Thus, each data point is characterized by 64 **features**, which are the integer values of the 64 pixels. Moreover, each data point is assigned a **label**, being an integer $0,\\ldots,9$, according to the digit which is shown in the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of datapoints: 1797\n",
+      "\n",
+      "Number of features used to characterize a data point: 64\n",
+      "\n",
+      "Number of different classes (different values the label can take on): [0 1 2 3 4 5 6 7 8 9]\n",
+      "\n",
+      "Number of labeled data point: 1797\n",
+      "(1797, 64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print number of datapoints\n",
+    "print(\"Number of datapoints: {}\".format(digits.data.shape[0]))\n",
+    "\n",
+    "# print the number of features used to characterize a single data point\n",
+    "print(\"\\nNumber of features used to characterize a data point: {}\".format(digits.data.shape[1]))\n",
+    "\n",
+    "# print number of different classes (number of different values the label can take on) \n",
+    "print(\"\\nNumber of different classes (different values the label can take on): {}\".format(digits.target_names))\n",
+    "\n",
+    "# print number of datapoints with known label\n",
+    "print(\"\\nNumber of labeled data point: {}\".format(digits.target.shape[0]))\n",
+    "\n",
+    "\n",
+    "print(digits.data.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape of digits.images :  (1797, 8, 8)\n",
+      "first image :  [[ 0.  0.  5. 13.  9.  1.  0.  0.]\n",
+      " [ 0.  0. 13. 15. 10. 15.  5.  0.]\n",
+      " [ 0.  3. 15.  2.  0. 11.  8.  0.]\n",
+      " [ 0.  4. 12.  0.  0.  8.  8.  0.]\n",
+      " [ 0.  5.  8.  0.  0.  9.  8.  0.]\n",
+      " [ 0.  4. 11.  0.  1. 12.  7.  0.]\n",
+      " [ 0.  2. 14.  5. 10. 12.  0.  0.]\n",
+      " [ 0.  0.  6. 13. 10.  0.  0.  0.]]\n",
+      "shape of digits.data :  (1797, 64)\n",
+      "data :  [[ 0.  0.  5. ...  0.  0.  0.]\n",
+      " [ 0.  0.  0. ... 10.  0.  0.]\n",
+      " [ 0.  0.  0. ... 16.  9.  0.]\n",
+      " ...\n",
+      " [ 0.  0.  1. ...  6.  0.  0.]\n",
+      " [ 0.  0.  2. ... 12.  0.  0.]\n",
+      " [ 0.  0. 10. ... 12.  1.  0.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# digits.images returns an np.array of shape(m,8,8), which represent\n",
+    "# m different images each having 8x8 pixels with grayscale value in the range 0..16.\n",
+    "\n",
+    "print(\"shape of digits.images : \",digits.images.shape)\n",
+    "print(\"first image : \", digits.images[0,:,:])\n",
+    "\n",
+    "\n",
+    "# digits.data reutrns an np.array of shape (m,6), which represent\n",
+    "# m different images each represented by 64 numbers that are the \n",
+    "# grayscale values of the image pixels \n",
+    "\n",
+    "print(\"shape of digits.data : \",digits.data.shape)\n",
+    "print(\"data : \", digits.data)\n",
+    "\n",
+    "# note that the two different numpy arries digits.images and digits.data contain \n",
+    "# exactly the same information but in a slightly different form \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 100 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt # import matplotlib library for plotting\n",
+    "\n",
+    "# plot the 8x8 images of the first 100 data points \n",
+    "# each data point represents a handwritten digit\n",
+    "\n",
+    "fig, axes = plt.subplots(10, 10)    # create an array of subplots, 10 rows and 10 columns\n",
+    "axes_flat = axes.flatten()          # collaps 10 x 10 array into a 1-dimensional array of 100 elements \n",
+    "for i, ax in enumerate(axes_flat):  # iterate over array of subplots \n",
+    "    ax.imshow(digits.images[i], cmap='gray_r')  # for i-th subplot, show the image of the i-th handwritten digit  \n",
+    "    \n",
+    "plt.setp(axes_flat, xticks=[], yticks=[], frame_on=False) # remove ticks and frame in all subplots\n",
+    "plt.tight_layout(h_pad=0.5, w_pad=0.01)  # reduce whitspaces between plots\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "584dcd28b7b78711fd31f007531562e8",
+     "grade": false,
+     "grade_id": "cell-2c2a7ef97a6dd04c",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<b><center><font size=3>Features vs Labels</font></center></b>\n",
+    "\n",
+    "The **[\"Linnerud\" dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud)** contains physiological parameters (Weight, Waist and Puls) and exercise data (Chins, Situps and Jumps) for 20 athletes. We can model this as a machine learning problem by considering data points representing athletes.\n",
+    "\n",
+    "In the description of the dataset the exercise data is referred to as features while the physiological parameters are considered the labels of datapoints (athletes). However, in practice one can choose what is the quantity of interest (labels). For example, if the quantity of interest is the number of jumps, one can use the physiological parameters (weight, waist and puls) as features to find a predictor for the number of jumps that the athelete is likely to achieve."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Features: ['Chins', 'Situps', 'Jumps']\n",
+      "\n",
+      "Labels: ['Weight', 'Waist', 'Pulse']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load the linnerud dataset into the bunch object \"linnerud\"\n",
+    "linnerud = datasets.load_linnerud() \n",
+    "\n",
+    "# print features names\n",
+    "print(\"\\nFeatures: {}\".format(linnerud.feature_names))\n",
+    "\n",
+    "# print labels names\n",
+    "print(\"\\nLabels: {}\".format(linnerud.target_names))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "7aef2a54a98c601a50db77069e48fa79",
+     "grade": false,
+     "grade_id": "cell-2de74c0d8ab9e319",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "### Real-World (Large) Datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "87b0a6b1b4968bc429e01dafd194973b",
+     "grade": false,
+     "grade_id": "cell-9437de9563e1c37e",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "In addition to small datasets it is possible to fetch larger datasets using the library `scikit-learn`.\n",
+    "\n",
+    "**The Labeled Faces in the Wild face recognition** dataset is a collection of JPEG pictures of famous people collected over the internet (read more [here](https://scikit-learn.org/stable/datasets/index.html#the-labeled-faces-in-the-wild-face-recognition-dataset)). \n",
+    "The dataset can be used to test methods for face verification or [face recognition](https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html#faces-recognition-example-using-eigenfaces-and-svms) classification problems."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset contain: dict_keys(['data', 'images', 'target', 'target_names', 'DESCR'])\n",
+      "\n",
+      "Number of datapoints: 3023\n",
+      "\n",
+      "Number of features: 1850\n",
+      "\n",
+      "Labels: ['Alejandro Toledo' 'Alvaro Uribe' 'Amelie Mauresmo' 'Andre Agassi'\n",
+      " 'Angelina Jolie' 'Ariel Sharon' 'Arnold Schwarzenegger'\n",
+      " 'Atal Bihari Vajpayee' 'Bill Clinton' 'Carlos Menem']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.datasets import fetch_lfw_people # load data with sklearn.datasets \n",
+    "\n",
+    "# load the part of dataset with labeled images\n",
+    "# use only persons with at least 20 images in the dataset, resize each picture by 0.4 ratio\n",
+    "lfw_people = fetch_lfw_people(min_faces_per_person=20, resize=0.4) \n",
+    "\n",
+    "# print the keys of all (key,value) pairs in the lfw_people \n",
+    "print(\"Dataset contain: {}\".format(lfw_people.keys()))\n",
+    "\n",
+    "# print number of datapoints\n",
+    "print(\"\\nNumber of datapoints: {}\".format(lfw_people.data.shape[0]))\n",
+    "\n",
+    "# print the number of features used to charactize a data point\n",
+    "print(\"\\nNumber of features: {}\".format(lfw_people.data.shape[1]))\n",
+    "\n",
+    "# print label or category names of the first 10 data points \n",
+    "print(\"\\nLabels: {}\".format(lfw_people.target_names[:10]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the image from the dataset\n",
+    "plt.imshow(lfw_people.images[42], cmap='gray')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "f50c9ddb2cfa4c0c3e3f0ed0ba869bf6",
+     "grade": false,
+     "grade_id": "cell-9caa25de1ebab052",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "## Visualizing Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "b98334525a8233fe7868e995316086dc",
+     "grade": false,
+     "grade_id": "cell-e621d21ef7385ef9",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "A very useful first step in applying ML methods is to visually inspect the dataset. Let us load and plot the [wine](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html#sklearn-datasets-load-wine) dataset from `sklearn`. We then visualize this data using some plotting functions provided by the Python libraries `pandas` ([`pd.plotting.scatter_matrix()` function](https://pandas.pydata.org/docs/reference/api/pandas.plotting.scatter_matrix.html)) and `seaborn` ([`sns.pairplot()` function](https://seaborn.pydata.org/generated/seaborn.pairplot.html))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "afac56cf41399101b281d6d6dd3cf44d",
+     "grade": false,
+     "grade_id": "cell-5bd0c981a64e942a",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])\n",
+      "data:\t\t (178, 13) \n",
+      "labels shape:\t (178,)\n",
+      "------------------------------------------------\n",
+      "['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# `pandas` is a a fast and easy to use open source library for data analysis and manipulation \n",
+    "import pandas as pd\n",
+    "\n",
+    "# Loading wine dataset from `sklearn` datasets\n",
+    "wine = datasets.load_wine() # loading the wine dataset from sklearn datasets\n",
+    "# print keys of all (key,value) pairs in the bunch oject \"wine\"\n",
+    "print(wine.keys())\n",
+    "\n",
+    "X = wine['data']   # read in the value for the key \"data\"\n",
+    "y = wine['target'] # read in the value for the key \"target\"\n",
+    "\n",
+    "# print out the shape of the numpy arrays X and y \n",
+    "print('data:\\t\\t', X.shape, '\\nlabels shape:\\t', y.shape) # print number of elements along each dimension of \"X\" and \"Y\" \n",
+    "print('------------------------------------------------') # print some dashes\n",
+    "print(wine['feature_names']) # print the feature names in the dataset, key is a string, value is an array of string"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "510b32bf29302e24f3ce275555661914",
+     "grade": false,
+     "grade_id": "cell-0588aff4d9ae2688",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "The code snippet below generates scatterplots using different combinations of features for data points in the wine dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "4f58c82d45719d43bf84f63de7fc144c",
+     "grade": false,
+     "grade_id": "cell-c3201b3787204aa6",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# transform np.array X to pandas dataframe, choose only first 3 features\n",
+    "wine_dataframe =  pd.DataFrame(X[:,:3], columns=wine['feature_names'][:3])\n",
+    "\n",
+    "# plot pandas dataframe with histogram and scatter plots\n",
+    "pd.plotting.scatter_matrix(wine_dataframe, c=y, figsize=(8,8), s=120)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "b97e1652cd7494856e564d1ba79da2fa",
+     "grade": false,
+     "grade_id": "cell-026c5a0e8f9e5f57",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 618.625x540 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns # import seaborn library for plotting\n",
+    "\n",
+    "# transform np.array X to pandas dataframe, choose only first 3 features\n",
+    "wine_dataframe =  pd.DataFrame(X[:,:3], columns=wine['feature_names'][:3])\n",
+    "wine_dataframe['wine category'] = y\n",
+    "\n",
+    "# plot pandas dataframe\n",
+    "# the lines shows the density plot which is essentially a smooth version of the histogram\n",
+    "sns.pairplot(wine_dataframe, hue = 'wine category', vars=wine['feature_names'][:3])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "35425936feb61f1e273d79847e3c38fd",
+     "grade": false,
+     "grade_id": "cell-bd82f3e1536b9a6d",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id=\"Q5\"></a>\n",
+    "\n",
+    "## Learning from Data by Fitting a Model (Curve)\n",
+    "\n",
+    "The `Linnerud dataset` consists of data points that represent athletes doing some exercise. We might be interested in how specific properties (or features) of a person affect their performance. Let us try to predict the number of chin-ups that an athlete can do.  \n",
+    "\n",
+    "We can formalize this as a machine learning problem. The data points represent athletes who are characterized by the feature $x$ given by the body weight in kg. The quantity of interest (the label) $y$ of a data point is the number of chin-ups the athlete can do. Our goal is to find a predictor function $h(x)$ which takes the bodyweight $x$ as input and outputs a predicted label $\\hat{y}$ which estimates the number of chin-ups that the athlete should be able to do. \n",
+    "\n",
+    "Choosing a good predictor $h(x)$ from the space of all possible functions $h(\\cdot): \\mathbb{R} \\rightarrow \\mathbb{R}$ is challenging since there are [**so many** of these functions](https://en.wikipedia.org/wiki/Function_of_a_real_variable#Cardinality_of_sets_of_functions_of_a_real_variable). Therefore, we restrict ourselves to the space of linear functions\n",
+    "\\begin{equation}\n",
+    "h^{(w)}(x) = w \\cdot x. \n",
+    "\\end{equation} \n",
+    "The set of all of such functions, obtained for different choices for $w$, constitutes the hypothesis space of linear predictors. \n",
+    "Each function of this **hypothesis space** is characterized by a single number $w \\in \\mathbb{R}$. Once we specify this number (or weight), we can compute the function value $h^{(w)}(x)$ for any possible feature value $x$. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "68cf104d276d24fca7fc7c13ecb4d210",
+     "grade": false,
+     "grade_id": "cell-17cd17a07c82795a",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<img src=\"../../../coursedata/R1_ComponentsML/Hspace.jpg\" alt=\"Drawing\" style=\"width: 1000px;\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "82a72cca722bbc4fb6c240db9905795d",
+     "grade": false,
+     "grade_id": "cell-db073f7fcf8a47d7",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<a id='Bonus2'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <b>Bonus Task.</b> Hypothesis space. \n",
+    "    \n",
+    "Bonus task worth of 50 points.\n",
+    "    \n",
+    "Prepare a short video that explains the concept of a hypothesis space. The video must not be longer than 10 minutes. \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "4f73c535452f7b9796b809168f1c3259",
+     "grade": false,
+     "grade_id": "cell-682234cedb587efc",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='HypothesisDemo'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "    <b>Demo.</b> Hypothesis Space of Linear Predictor Functions.  \n",
+    "    \n",
+    "The code snippet below creates a scatterplot of the `Linnerud` dataset and also plots \n",
+    "some of the predictor functions from the linear hypothesis space. These predictor functions \n",
+    "are of the form $h(x) = w \\cdot x$ with given weight $w$.\n",
+    "\n",
+    "Hint: In this section, we will use the Python library [Scikit-learn (Sklearn)](https://scikit-learn.org/stable/index.html) to fit models to data, specifically we will use [linear regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn-linear-model-linearregression)\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "7d5828e73ea5e42aec482fd17df1bb31",
+     "grade": false,
+     "grade_id": "cell-6fadac9f2f063914",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the library \"numpy\" provides functions for matrices and vectors\n",
+    "import numpy as np\n",
+    "# import matplotlib library for plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "# import toy datasets from sklearn library\n",
+    "from sklearn import datasets \n",
+    "# import module for linear regression \n",
+    "from sklearn import linear_model \n",
+    "\n",
+    "# load Linnerud dataset into bunch object `linnerud`\n",
+    "linnerud = datasets.load_linnerud()  \n",
+    "# read out the values for the key 'data' and store them in the numpy array ChinUps \n",
+    "ChinUps = linnerud['data']  \n",
+    "# read out the values for the key 'target' and store them in the numpy array Weight\n",
+    "Weight = linnerud['target']  \n",
+    "\n",
+    "# we use the weight (in Lbs) of each athlete as features \n",
+    "x = Weight.T[0] \n",
+    "# we use the number of chin ups performed by each athlete as label \n",
+    "y = ChinUps.T[0] \n",
+    "x = x.reshape(-1,1)  # convert to numpy array of shape (m,1)\n",
+    "y = y.reshape(-1,1)  # convert to numpy array of shape (m,1)\n",
+    "x = x*0.453 # convert Lbs to Kg\n",
+    "\n",
+    "# initialize the linear regression model\n",
+    "reg = linear_model.LinearRegression(fit_intercept=False)\n",
+    "# fit the linear regression model with variables \"x\" and \"y\"\n",
+    "# to create the reg.coef_  (weight) attribute \n",
+    "reg.fit(x,y)\n",
+    "\n",
+    "# initialize and empty list\n",
+    "hypothesis_space = [] \n",
+    "# generate 10 linear predictors\n",
+    "for i in range(10): # loop over range 0-10\n",
+    "    reg.coef_ = np.array([[i*0.05]]) # set new regression coefficient (weight)\n",
+    "    n = reg.predict(x) # make predictions based on the previously defined and fitted regression model\n",
+    "                       # but with new regression coefficient (weight)\n",
+    "    hypothesis_space.append(n) # append the preditions to \"hypothesis_space\" list\n",
+    "\n",
+    "# plot the datapoints and generated predictor functions from the linear hypothesis space\n",
+    "# initialize subplots and get \"fig\" and \"axes\" objects\n",
+    "fig, axes = plt.subplots(1, 1, figsize=(12, 6))  \n",
+    "\n",
+    "# initialize a scatterplot\n",
+    "axes.scatter(x, y, color='blue',label=\"data points\") \n",
+    "# plot items from the \"hypothesis_space\" list\n",
+    "for i in range(len(hypothesis_space)): # loop through the items in \"hypothesis_space\" list\n",
+    "    y_n = hypothesis_space[i] # plot the ith item from the \"hypothesis_space\" list\n",
+    "    l = 'w = {:.2f}'.format((i)*0.05) # get a formatted string to use in legend\n",
+    "    axes.plot(x, y_n, label=l) # add the item from \"hypothesis_space\" to the plot\n",
+    "\n",
+    "plt.rc('legend', fontsize=10) # update plot fonts\n",
+    "plt.rc('axes', labelsize=20)  # update plot fonts\n",
+    "plt.rc('xtick', labelsize=20) # update plot fonts\n",
+    "plt.rc('ytick', labelsize=20) # update plot fonts\n",
+    "plt.rc('font', size=20)       # update plot fonts\n",
+    "\n",
+    "axes.set_title('Several different linear predictor functions') # set plot title\n",
+    "axes.set_xlabel('body weight (kg)') # set x-axis label\n",
+    "axes.set_ylabel('number of chinups') # set y-axis label\n",
+    "axes.legend(loc='upper left') # set location of the legend to show in upper left corner\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "96761712f1206bee47e44ec297015121",
+     "grade": false,
+     "grade_id": "cell-1ed770e7af6187b0",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "\n",
+    "### Learning the Best Predictor by Fitting a Model\n",
+    "\n",
+    "A key idea underlying ML is to choose predictor functions $h(x)$ based on how well they fit historic or training data. For linear predictors, of the form $h(x)=w \\cdot x$, the search of a good predictor reduces to the search for a good choice for the weight $w \\in \\mathbb{R}$ which is just a number. \n",
+    "\n",
+    "To search for a good predictor function $h$, which maps a feature value $x$ to the predicted label $\\hat{y}=h(x)$, we need to measure the loss (or error) incurred when the true label is $y$ but the predicted label is $\\hat{y}$. There are many different choices for how to define such a loss function. \n",
+    "\n",
+    "In general, we are free to define the loss function to best suit the application at hand. However, for certain classes of machine learning problems some useful choices for the loss functions have crystalized. For example, if the labels of data points take on numeric values, a widely used choice for the loss function is the squared error loss $(y - \\hat{y})^{2}$. \n",
+    "\n",
+    "Using loss functions to measure the quality of a predictor requires the availability of data points for which we know the true label $y$. One option to get labeled data is from historic recordings or experiments. Assume we have some labeled data points $(x^{(1)},y^{(1)}),\\ldots,(x^{(m)},y^{(m)})$ consisting of $m$ data points. The $i$th data point has the feature $x^{(i)}$ and the true label $y^{(i)}$. \n",
+    "\n",
+    "We have now all the tools to find the best linear predictor $h(x) = w \\cdot x$ by minimizing the empirical risk or average squared error loss \n",
+    "\\begin{equation}\n",
+    "(1/m) \\sum_{i=1}^{m} (y^{(i)} - \\hat{y}^{(i)})^{2} = (1/m) \\sum_{i=1}^{m} (y^{(i)} - w \\cdot x^{(i)})^{2}. \n",
+    "\\end{equation}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One might wonder if we could use the prediction error $y - \\hat{y}$  itself as a loss function. It can be easily shown that the prediction error (without squaring) is not a good choice as loss function. Indeed, ML methods try find a predictor which minimizes the (average) loss incurred over some labeled data points (training data). Using the prediction error as loss, this minimization problem would result in a trivial predictor $\\hat{y}$ which outputs a number as large as possible (limited by the used number format). Indeed, making $\\hat{y}$ as large as possible would \n",
+    "make $y-\\hat{y}$ as small as possible. The resulting predictor would completely ignore the training data and therefore will not be useful at all. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "0aa33673c574e274f8a855f517a6a982",
+     "grade": false,
+     "grade_id": "cell-cb8925a42313f816",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='FitRegressionDemo'></a>\n",
+    "<div class=\" alert alert-info\">\n",
+    "    <b>Demo.</b> Fitting a Linear Model to Data.  \n",
+    "\n",
+    "A linear model corresponds to the set of linear predictor $h(x) = w \\cdot x$ with some weight $w\\in \\mathbb{R}$. Fitting a linear model means to choose the weight to minimize the average prediction error $y-h(x)$ incurred for some labeled data points. The optimal weight $w_{\\rm opt}$ can be computed via the function `LinearRegression.fit()` and the corresponding prediction $\\hat{y} = w_{\\rm opt} x$ for a data point with feature $x$ can be computed using `LinearRegression.predict()`. \n",
+    "\n",
+    "We will plot the data points along with the predictions $\\hat{y}^{(i)} = w_{\\rm opt} x^{(i)}$ and the prediction errors $y^{(i)} - \\hat{y}^{(i)} = y^{(i)} - w_{\\rm opt} x^{(i)}$ as red bars. \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "3140eb043c9d0a59f04e65c0b96d5ed0",
+     "grade": false,
+     "grade_id": "cell-1ad68ef75f613b5c",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "optimal weight w = 0.11143528587026878\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": [
+    "# plot datapoints, linear predictor with optimal weight and prediction errors  \n",
+    "# we will use values of variables `x` (features, weight) and `y` (labels, chin ups) \n",
+    "# generated in the previous cell \n",
+    "\n",
+    "plt.rc('font', size=20) # set plot font\n",
+    "\n",
+    "reg = linear_model.LinearRegression(fit_intercept=False) # initialize Linear Regression model\n",
+    "reg.fit(x, y) # fit the linear regression model using \"x\" and \"y\" variables\n",
+    "y_pred = reg.predict(x) # make predictions based on fitted model\n",
+    "\n",
+    "# print weight of the optimal predictor \n",
+    "print(\"optimal weight w =\", reg.coef_[0][0])\n",
+    "\n",
+    "# initialize subplots and get \"fig\" and \"axes\" objects\n",
+    "fig, axes = plt.subplots(1, 1, figsize=(8, 4))\n",
+    "# initialize a scatterplot with horizontal (vertical) axis representing feature (label) values \n",
+    "axes.scatter(x, y, label='data points') \n",
+    "# add the predicted labels \"y_pred = h(x)\" made by the model to the plot\n",
+    "axes.plot(x, y_pred, color='green', label='optimal linear predictor') \n",
+    "\n",
+    "# indicate error bars\n",
+    "\n",
+    "axes.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): # loop through range length of x - 1\n",
+    "    lineXdata = (x[i+1], x[i+1]) # make tuples with same X\n",
+    "    lineYdata = (y[i+1], y_pred[i+1]) # make tuples with different y's\n",
+    "    axes.plot(lineXdata, lineYdata, color='red') # add the red lines to the plot to indicate error distance from our predicted regression model\n",
+    "\n",
+    "# add legend to the plot and set position\n",
+    "axes.legend(loc='upper center', bbox_to_anchor=(1.4, 1.05),fontsize=20) \n",
+    "# set axes labels\n",
+    "axes.set_xlabel(\"feature x (body weight)\")\n",
+    "axes.set_ylabel(\"label y (number of chin-ups)\") \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[9.64171524]\n",
+      "[5.]\n",
+      "[4.64171524]\n",
+      "optimal weight w = 0.11143528587026878\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": [
+    "# plot datapoints, linear predictor with optimal weight and prediction errors  \n",
+    "# we will use values of variables `x` (features, weight) and `y` (labels, chin ups) \n",
+    "# generated in the previous cell \n",
+    "\n",
+    "plt.rc('font', size=20) # set plot font\n",
+    "\n",
+    "reg = linear_model.LinearRegression(fit_intercept=False) # initialize Linear Regression model\n",
+    "reg.fit(x, y) # fit the linear regression model using \"x\" and \"y\" variables\n",
+    "y_pred = reg.predict(x) # make predictions based on fitted model\n",
+    "\n",
+    "print(y_pred[0])\n",
+    "print(y[0])\n",
+    "\n",
+    "print(y_pred[0]-y[0])\n",
+    "\n",
+    "# print weight of the optimal predictor \n",
+    "print(\"optimal weight w =\", reg.coef_[0][0])\n",
+    "\n",
+    "# initialize subplots and get \"fig\" and \"axes\" objects\n",
+    "fig, axes = plt.subplots(1, 1, figsize=(8, 4))\n",
+    "# initialize a scatterplot with horizontal (vertical) axis representing feature (label) values \n",
+    "axes.scatter(x, y, label='data points') \n",
+    "# add the predicted labels \"y_pred = h(x)\" made by the model to the plot\n",
+    "axes.plot(x, y_pred, color='green', label='optimal linear predictor') \n",
+    "\n",
+    "# indicate error bars\n",
+    "\n",
+    "axes.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): # loop through range length of x - 1\n",
+    "    lineXdata = (x[i+1], x[i+1]) # make tuples with same X\n",
+    "    lineYdata = (y[i+1], y_pred[i+1]) # make tuples with different y's\n",
+    "    axes.plot(lineXdata, lineYdata, color='red') # add the red lines to the plot to indicate error distance from our predicted regression model\n",
+    "\n",
+    "# add legend to the plot and set position\n",
+    "axes.legend(loc='upper center', bbox_to_anchor=(1.4, 1.05),fontsize=20) \n",
+    "# set axes labels\n",
+    "axes.set_xlabel(\"feature x (body weight)\")\n",
+    "axes.set_ylabel(\"label y (number of chin-ups)\") \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "3e71b26b8e707c2d02b6a3ce390ea3fd",
+     "grade": false,
+     "grade_id": "cell-16037415de0db9f3",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "### Adding an Intercept Term \n",
+    "\n",
+    "A simple but useful extension of the linear prediction functions used above is to add an intercept term. In particular, we consider predictor functions of the form \n",
+    "\\begin{equation}\n",
+    "h(x) = w \\cdot x + b \n",
+    "\\end{equation}\n",
+    "which involves a weight $w$ and a constant offset $b$. The offset $b$ is sometimes referred to as the \"intercept term\" and geometrically it is value at which the regression line crosses the y-axis. The code snippet below finds (or learns) the best choices for the weight $w$ and intercept $b$ in order to minimize the average squared error incurred for a given set of labeled data points $(x^{(i)},y^{(i)})$. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "38536662550b1d5300a59aea24c8a45c",
+     "grade": false,
+     "grade_id": "cell-765366fec7df14e4",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model without intercept: optimal weight w = 0.11143528587026878\n",
+      "model wit intercept: optimal weight w = -0.18418114215330775 and intercept = 24.351322650827086\n"
+     ]
+    }
+   ],
+   "source": [
+    "# build linear models without (fit_intercept=False) and with intercept (fit_intercept=True)\n",
+    "\n",
+    "# initialize Linear Regression model without intercept (fit_intercept=False) \n",
+    "reg = linear_model.LinearRegression(fit_intercept=False) \n",
+    "reg.fit(x, y) # fit the linear regression model using \"x\" and \"y\" variables\n",
+    "y_pred = reg.predict(x) # make predictions based on fitted model\n",
+    "# find the optimal weight for linear regression model (without intercept)\n",
+    "w_opt = reg.coef_[0][0]\n",
+    "\n",
+    "# initialize Linear Regression model with intercept (fit_intercept=True)\n",
+    "reg_intercept = linear_model.LinearRegression(fit_intercept=True) \n",
+    "reg_intercept.fit(x, y) # fit the linear regression model using \"x\" and \"y\" variables\n",
+    "y_pred_intercept = reg_intercept.predict(x) # make predictions based on fitted model\n",
+    "# find the optimal weights for linear regression model (with intercept)\n",
+    "w_opt_intercept = reg_intercept.coef_[0][0]\n",
+    "# find the intercept for linear regression model (with intercept)\n",
+    "intercept = reg_intercept.intercept_[0]\n",
+    "\n",
+    "# print parameters of the optimal predictors\n",
+    "print(\"model without intercept: optimal weight w = {}\".format(w_opt))\n",
+    "print(\"model wit intercept: optimal weight w = {} and intercept = {}\".format(w_opt_intercept,intercept))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "ed04c708c23276285c525761f83b65d6",
+     "grade": false,
+     "grade_id": "cell-15abbfeb454adb97",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "Note that the predictor function obtained without intercept term passes through origin, while the predictor function obtained with the intercept term crosses y-axis at the value of the intercept term."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "fa37ca2aa95fc737b8e270673e23659f",
+     "grade": false,
+     "grade_id": "cell-61b30db798a56dce",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "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": [
+    "# plot regression dataset\n",
+    "plt.rc('font', size=20) # set plot font\n",
+    "\n",
+    "# create a grid of test feature values  \n",
+    "x_grid = np.linspace(-10, 120, num=100).reshape(-1,1) \n",
+    "# compute predictions from linear regression model without intercept term \n",
+    "y_pred = reg.predict(x_grid) \n",
+    "# compute predictions on test feature values using linear regression model with intercept term \n",
+    "y_pred_intercept = reg_intercept.predict(x_grid)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 1, figsize=(8, 4))\n",
+    "# initialize a scatterplot \n",
+    "axes.scatter(x, y, label='data points') \n",
+    "# add the predicted labels \"y_pred = h(x)\" made by the model to the plot\n",
+    "axes.plot(x_grid, y_pred, color='green', label='no intercept') \n",
+    "# add the predicted labels \"y_pred_intercept = h(x)\" made by the model to the plot\n",
+    "axes.plot(x_grid, y_pred_intercept, color='red', label='intercept')\n",
+    "\n",
+    "# set axes labels\n",
+    "axes.set_xlabel(\"feature x\") \n",
+    "axes.set_ylabel(\"label y\")\n",
+    "\n",
+    "# add legend to the plot and set position\n",
+    "axes.legend(loc='upper center', bbox_to_anchor=(1.25, 1),fontsize=20) # add a legend to the plot\n",
+    "\n",
+    "axes.axvline(0,c='k',ls=':') # add dotted line at x=0 to the plot\n",
+    "axes.axhline(0,c='k',ls=':') # add dotted line at y=0 to the plot\n",
+    "axes.axhline(intercept,c='k',ls=':') # add dotted line at y=intercept to the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "2d58cefcc2b648ed46ad3a3d4286c38e",
+     "grade": false,
+     "grade_id": "cell-0ff92b94c941f4be",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "Note that the optimal weight for the linear predictor without intercept is positive, which suggests a positive correlation between feature value and label value (higher feature value hints at higher label value). In contract, the optimal weight obtained for the linear predictor with an intercept term is negative. This negative weight would suggest a negative correlation between feature value and label value (higher feature value hints at lower label value). \n",
+    "\n",
+    "Since the training error obtained from the linear predictor with intercet term is smaller, it is more plausible to have a negative correlation between feature value and label value. This agrees with the intuition that having a higher body weight typically implies a smaller number of achievable chin-ups. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "81c9cd0dbe86680040851d5088f91f56",
+     "grade": false,
+     "grade_id": "cell-11aeade9e7ce5633",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<a id='QuestionR1_1'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <b>Student Task.</b> Fitting a Linear Model without and with Intercept. \n",
+    "\n",
+    "Fit a linear regression model to a set of data points $(x^{(1)},y^{(1)}),\\ldots,(x^{(m)},y^{(m)})$ which are generated synthetically (using a random generator) and stored in the vectors `syn_x` and `syn_y`. Use the Python class `linear_model.LinearRegression` to represent the set of linear predictor functions. Find optimal weight for linear predictor without intercept `w_opt` and optimal weight and intercept `w_opt_intercept` and `intercept` for linear predictor with intercept. Plot regression dataset.\n",
+    "\n",
+    "Use `LinearRegression(fit_intercept=True)` when initializing the Linear regression model with intercept and `LinearRegression(fit_intercept=False)` when iinitializing it without and intercept.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "6f7d83a7c4ae8e9732dd7831ec81cabc",
+     "grade": false,
+     "grade_id": "cell-e7ae566693783d23",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model without intercept: optimal weight w = 0.5078222136746467\n",
+      "model wit intercept: optimal weight w = 39.19060597876406 and intercept = -388.47209806720974\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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M2TgTcuVK9jWTXNJdZGmShAhTM2bMAODjjz+2OBIhhJmEVpo45CGvNS2vHOI/W7+n0qXjXMmZn9HvdePH8vUo8Ew+hqQgAREiliQhwtS2bdsASUKEyMjMVpQEh0RQcsj/Uj7UERMDGzfC2LFM2b+f4NzPMLxub1aXrc2DbNllDodwCElChKmNGzdaHYIQIhFF8rqbllHXJH14JnZeyZVbYbS96M+gfavJc+o4YcVeYGKjAax6tQaRzi4AKHh8dYwQKSRJiBBCZGL2anjEl9jwzPoDwQxfE0Cdg9vps/snXrp1kdMFinNyzAw+Ua9y/u7Dx9pr4Pfj1x35ZYinlCQhwtSUKVMAGDRokMWRCCES4prNKdG9XUwLgT18yLGx3/Df7SsoEXKZY8+UoHezIWx5+S0KZ8sphcVEmpIkRJjavXu31SEIIRIQf2UMGEMl2k7bJwqB3b9vlFH/+muGnj/PoUIv0a3FcHxeeh2tjBJSsctn7Q33pKSwWFovJRaZjyQhyaSUKgZ8BdQHCgCXgfXAaK31bStjc7S1a9daHYIQIgH2VsZonkxEHptEeu8eLFgAkyfD5ctQtSqf1OrFumfLgK0iaqzYRMFeyfbkTkpNj6XEIvORiqnJoJR6EdgPdAb2At8AZ4CPgd1KqQIWhieEeMqYDYloeHJjuZdyGzvaliwJn3wCr7wC27fDzp2807897tkf/5k0NtFoXqEoE1qUtb9RXTLIpnXCniT3hCilXLTWkWkZTCYwF3gW6K+1nhV7UCk1DRgIjAN6WhSbw02cOBGAIUOGWByJEE+XpAxbrD8QjJNSj0qpx1U0rzu7htQy3ty+DbNmwfTpxu/r1zfKq1er9qh9YhVMHVFYTOaWpA2tNTE6Bo3tNd57e8di35u1i9ExlMxXEieV9v0UStv5B2y3oVLXgKXAQq31qTSNKgNSSpUCTgNBwIta65g4n+XCGJZRwLNa63tm16lcubL29/dP42gdo02bNgCsXLnS4kiEyPjsfWNP6IFg1nbL4ctM3HKM+1FRGH0aMbhmc+KTOqWp9eozxOgYth27wnSfEzyIikYT+60oBtC4ZlP0rFmKmgWcKbLoJ4p8v45sd+9xvXZVzvZpS2jZl5+IKaUPqqR+nRrN7O0nCb3/0BYntrg1udyc6VKthN1rmsWY6EM2BQ/g9PgzMGuXmmumlZDPQ8jjlsdh11NK7ddaV37ieDKSkBtAfoz/FduB+cAGrXWUw6LMwJRSXYGFwAKtdQ87n/8K1AVqa623mV0nMyUhmVH8b1xJ+abi6G8K8a9h794JXdOsXWq+ntTGmOx7kbIHsKP+DNL74ZOWD4Pkeu4uDPKDXv7gHglr/gXj3oFDhayOLGEKhZNywkk5oZRCoVBK4aycHzvm7OT8qK1Scc6xtY99H3ssbjt793B2crZ7ftz72zs/7jXsXTPRGHF64vPkxJycP4OUfF1tyrQhu3N2x/39miQhyZmYWgRoBXQH3gNqAdeUUkuARVrrsw6JNOOKnYV1wuTzkxhJyMuAaRISGBjIsmXL6NSpE5GRkdSpU4euXbvSrl07wsPDadiwIb169aJ169aEhobSrFkz+vfvT4sWLbhx4watWrXi008/pUmTJuw8spPW/2lNxVYVKV6pOHeu3WH7tO14ve9F4fKFuXPlDn/O+pPybcrz7GvPEnoxlD3z91C2bVkKeBbg9rnbHFhygDIfliHvi3m5ffY2/3z/D/9q9y9yvZCL26dvE/hjIJ4dPPEo6kHIiRBOrzlN6Y6lcSvkxu3jt7mw/gIlO5Uke8HshB4N5dLmSzzf6Xlc8rkQejiUa79do1jnYjjndubOoTvc2n6L5zo/h1NOJ+4G3OXOH3co2KUgyl1x7+973PO7R96P8qJdNBH7I7i/7z45PsqBdtI89H/Iw78f4tbVjRgdQ9S+KKL/icapo5PxQNgTY/zttLP9Yf+FMWOnre39LuAi0Nr2/k/gCvC+7f0O4AbQ0vZ+O3AHaG577wOEA01t738FooBGtvex+/01sL3+D+N/WD3b+42AB1Db9n49kBvjfxLAWqAgUMP2fjVQCKhue78KKAbE9qKvAEoBb9ree2P863vd9v47oAxQyfZ+KeAFVACige+BikB54CHwA1AFnMo6oR4oYlbE4PymMy5lXCAcHvz4ANe3Xcn+r+wQBvd+vId7TXfcXnFDh2ru/HiHXHVy4e7pTvTNaG6tvEW+BvnwKO1B1LUorq+6TsEmBcn5Yk4eXH7AlZ+uUPjfhclZMif3L94neHUwxT8oTs7ncxIeFM6FNRco+WFJchXPxd1TdwlaG8QrnV4hR5EchAaGcmbdGcp0K0OO53Jw+8htTq4/SaU+lfAo4MH1g9c5sfEEbw54E/e87lzef5kTm05QfXB13PO4c+GvCxz/33HqDKuDa05Xzu48y7FfjtHwy4Zkd8/Oyd9Pcuy3YzQf15xsLtk4vvU4R3yO8J8p/0GhOPTLIY77HufDKR+iUOzfuJ+Tu0/SYVIHlFLsXrObM/vP0OnrTiil+GPFH5w/ep7O4zvjpJzwWe7D0YNB6Aa1CA2PQu85QMHI+3z2TW+GrP2H8B1/EXM3jJyN6wOK8N//JCbiPt97f4FC0bHLFHRUFLnqNQAUYVt/I8fDh0x0caHTPwH0exjJpy8Wp+PcURQsXYLiw6ZRRuWh5+c9cVJOjB80nueKPEfPwT3548QNxg0cTVTuArxYvzmdqpbk1+nT8CzjSY+Pe6CU4uPOH1OhcgW69euGk3Kie9vuVKtRja69u6JQtG/ZnroN69KlexeclBMtG7Xk363+TaePOuGknGhUpxEfdviQfF61mbblOAcXDKHYm434ekgf6nsWoFGjRkn+vnflyhXatGnDkCFDqF+/PhcuXKB9+/YMHz6c2rVrc+bMGbp06cLo0aOpUaMGgYGB9OjRg/Hjx1O1alUOHz5M3759mTx5MlWqVCEgIIABAwYwffp0vLy82LdvH4MHD2b27NmUKVMGPz8/hg0bxtxv5+Lp6cmOHTv48ssvWbJkCaVKlcLHx4exY8eyfPlyihcvzpYtW5g4cSIrV66kUKFCbNq0ialTp7JmzRoKFizIunXrmDlzJhs2bCBPnjysWrWKefPmsXnzZjw8PPD29mbRokVs3boVFxcXli1bxrJly/D19QVg4cKFrFq1Ch8fHwDmzp3Lpk2bHm04OmPGDLZt2/ao2OSUKVPw2+33aKHBxIkTCQgIeNTDPWbMGAIDA/H29gZg5MiRXLhwgQ5LOwAwdOhQbt68yYIFCwCjZENERARz5swBYMCAAQBMnz4dgD59+uDu7v6oxEP37t0pUMB8umSSkxCt9UOMb3srlFIvAz2ADsBQ4HOl1G/At8CmuEMVWUhsv1Soyeexx5/YRlIp1R0jecPV1dVhAV0Pv861e9fYcW4Hbu7GgyA0PJS9l/biltON6FvRhNwP4dDVQ7jncifqWhR3Ht7hxM0T5Liagwe3HhAeGc750PPcun2L+2H3eRj1kJsRN3kQ/oCgbUHcu36PiMgInKOcidbGpDKFIrtzdlydXXF2ciaXay5yeuREuStuON+gSK4i5Mibg2u5rhHmEkbpAqXxyOfB5XyXeZj9IRULV8QjrwcXgi9w3O0477zwDu453Tl76SzHPY7T4OUGuLq7cuLqCY4dPUbzss1xcXHh2PVjHD1xlNaVW+OknDh08xCB5wNpW60tSin+vvU3J2+cpH3N9igUf938izN3ztDxvY4oFH9e+5PzD8/TuW5nlFJsu7aNS+oSXRp0QSnFlstbuOp2lW5NuqFQbAjewO2rt+nW3Hi/+vxqwkLD6N6yOwrFD0E/8PD+Q3p8YHyjXnJmCQpFj7Y9UCjmnpyLq5srPdv3RKH45vg35MmXhx6deuCknJhwZALPFXmOHh8Z53958EtKvFiC7t2746Sc+PzA57xS5hW69zbe99/Xn4pVKtK1X1fjQfCX7UHQqytKKdrvbE/d94wHgVKKVr6taFGvBR27dMRJOdF4W2M+bPwhHTp2IDoqmkY7GtGleRfatWvH/Yj7NN7VmF6t4jwIdjWj//txHgS7WvHp+3EeBDvbMOT9OA+Cne0Z3irOg2BnF0a3ivMg2NmD8a3iPAh29mVyqzgPgp0DmN4yzoNg12Bmt4jzINg1jG///e3/Pwh2fcmS5nEeBH5jWdx0sfEgyLuFibsnsrDJQuNB4LaJqX9NZX7j+caDQK9j5p6ZzG4023gQRKxi3t55zGw403gQ3PZmkf8ivqn/jfEguLKMZfuXManOJAAWBi1k1aFVjK01FoC5x+eyKXATI2qMAGDGgRlsC9rG529/DoDTbid2X9nNwLcGAuC/4grXb98h/9265AJCHoRyNeQSBVUDirs+z1l9jaiYG+SMNjLUSH0Od5cIPnjtAwAGuK7mTmQ4HjFvUSzkCrmCrvLSjfN0dHLCuVNH1IMH5CxUiLfqdgFgZa6VFMhTgOovVGf9gWD2nYnmYXAEfy8K4d5DiIz0IJvOT8idoszY8hCPm1AjZyFefeZVAHJmz0nBHAV5Mf+LALi7uJPPPR/FchcDwDWbK7ldc/NsjmcBcHF2IUf2HOR1M74dOjs545bNjdaVX6JF+Reo4zOFrk3L0rLi84SHhzvse6LIfJI8HGP3ZKWyY/SOdMP4+U1jzI1YhDFscckRQWYESqkFGF9nN631Ijufj8dIyIZqrSeaXSczDce0a2d0KcRmyEIIx6g2cbvd2hv5PFwIux9FZMzj35ddnBWTW5UHjMmjwSERlLoVTO/dP9H8yO9EOznxc/m65P9qBHUbvfnEdWPZqytijwK+ae0lS2eFwzhiOOYJWuuHSqn/YXQil8YYsikCjASGKqXmAZ9rrR+k5j4ZRGxPh9lMndzx2mV6knwI4ThxV7yY/eh3O9z+AsTIaM3oTUcIux9FyatnmbF7NY2P/0mkcza+r9iYDXXa0vmDt6mbSNJgb5msPdrWVpIQkdZSnIQopd7EGJL5AHDDGD2fCSzBGGn+BOgHuAK9Uh2p9WIXs79s8nlp26vZnBEhxFMqqT0QCSly9jh9/VbR4IQf91zcWFilOYte/zeuRYv8/3LcRCRnOawsnRXpIVlJiG0panuM5KMMRq/d38A8YIXWOvZf7SGl1HJgC8ZwTVZIQn63vdZVSjnZWaJbDYjAmA6ZJYwcORKAr776yuJIhMjcktIDoQA3FyciIh+fUlf+UiD9/FZS+/Q+7mT3YOZbrVlSpRkh7kbnq0pGspDQjrv22gqR1pJTrGwRxroCD+ABsByYq7Xea6+91jpaKeXL/8/9z9S01qdtk2/rAn2AWXE+Hg3kAL5NqEZIZnPhwgWrQxAiS0hKr4IGomI0Lk6KyBhNlQuH6ee3ineCDnDbLRdTqrfj+4qNueOW87Hz8ri7JDkOeyXYnYD4KwlSUpZdiJRITk9IF4xiXfOBpVrrW0k4xxdjn5WsojfgB8xUSr0HHAPeAN7FGIb5wsLYHG7p0qVWhyBElpDUHojIqBjqXT1Cjz9+pOLZg1z3yMv4mp3xrtCQ8Oz2eybuPYxi/YHgJM3fMKuMau+YzAcR6SE5xcrqaa1/TeN4MjylVHHMN7BLNDHLTKtjhBCOkeicEK2pecaf/n4rqXgpEIoU4VDb7nR0Ls9tEu/peKxMuxAZUKpXx0gCYtBaX8DYwC7LGzp0KAATJkywOBIhMjd7PRD3HkQRGv6Auif/oq/fKspePc3F3M8yufkABq+cSDlXV76Mt4eMWW+KTCIVmVWqluiKrO3mzZtWhyBElvHYJnDR0eybvIA8cybx8vUgzuYrzOAG/dniVYcx71cAW1HD+BvHmdUXkUmkIrOSJESYii3TK4RIvfUHgpm2+ShVdv9C/71rqHL9AndKvcyoWsPwfv4NnsufkzGJzMWwN7FUJpGKzEySECGESGMb9p7l79HT8d61iudDr3L02ZJ83HIY7w7tyahKxRmVxOvEHdYJDonAWSkiIqOZ/GvgY58LkVlIEiJMDRo0CODRRkRCiGS6fx8WL+aNL76iWeg1AgqXZnTt7mx78XVQCv+tJ2leqXiyLhmbaMTtEQkOiWDoun8e+1yIzECSEGEqIkImuwmRIvfuwbffwuTJcJrNKCcAACAASURBVOUKF4r+i8/q9uGPkhVBqUfN4k4oXR9vEmpCy2TtFT+L7RGRJERkJpKECFOxWzULIZLozh2YMwemTYMbN6BWLfjxRwbsjiY49P4TzWMnlMZfwptYz4bZahhZJSMyGyerAxBCiEzv9m0YPRpeeAGGDYPKlWHXLti2DWrWZHD9V3B3cX7slLgTShPq2bDHbDWMrJIRmY1pT4hSakkKr6m11h+l8FyRgQwYMACA6dOnWxyJEBnUjRvwzTcwaxbcvQvNmsHw4UYSEodZpdLY48nt2ZBVMiKrSGg4plMKr6kBSUKEEFnXlSswZQrMmwcREdCqlZF8lCtnekr8mh9xmRUiM+vZSCypESKzMC3brpR6IaUX1VqfS3FEWZyUbRciE7twASZNgoULITIS2rY1hl9efTVVl7VX1t3dxZkJLcpKYiGyhGSXbZdEQgghbM6cgYkTYdky0Bo6dIChQ+GllxxyeenZEE8rWR0jTPXp0weQVTLiKXbiBIwfD97e4OwM3brBZ58ZE1AdLKHhGiGyqmQnIUqpJsCHwKtADq31S7bjrwJNgB+01sEOjVJYwt1dZtqLp0tsrY6cJ48xyH8ttQ/vQLm6Qr9+MHgwFClidYhCZClJTkKUUgpYBrSzHYoA4j6lbgPjAQV87aD4hIWkUqrIquwVBgNYPudnhv/5Iw1O+BGW3Z1Fb7ak6Fdf0PC98mlyT+n5EE+75PSE9AbaA0uAT4GBwIjYD7XWV5RSu4BGSBIihMig7BUGWznzJ3r7rWTtib3ccc3BjKptWFq5KSHuuSm67yYN33P8PaXMuhDJS0I+Ag4C3bTWWillb1nNSaCeQyITluvevTsgu+mKrCVuYbDXLxym366VVD8XwC333Eyu3p7vKzXmrmuOR+0dUYVUyqwLYV9ykhBP4FtttqbXcA14JnUhiYyiQIECVocghMNduh3O20EB9PNbyRsXj3DdIy/janbhhwoNCM/+5DwoR1QhlTLrQtiXnCQkCnBLpE1RICzl4YiMZMKECVaHIITjaA2bN/PfHz/jtQvHuJyzAKPe686P5evxwMWVvO4u6KiYNKlCmtxiZEI8LZKzd8xRoKZtguoTlFJuQC3ggCMCE0IIh4iJgXXroFIlaNyYklF3GdWgLzV6LGJZ5aY8cHHF3cWZUU1fY0KLshTN644CiuZ1d1ixsMH1PBPcO0aIp1VyekKWA7OBb5RSn8T9QCnlDEwDigBDHBeesFLnzp0BWLp0qcWRCJEC0dHw008wbhwcOWIUFluyBI927fA6fI2tJitV0mKOhhQjE8K+5CQh3wJNgf7A+8BdAKXUGuBNjARkg9b6B0cHKaxRvHhxq0MQIvkiI2HFCqPI2IkTRkl1b29o3RqyGd/yrCgMJsXIhHiS6d4xdhsrlQ0YDvQB4s5aDAFmAWO01lEOjTCLkb1jhEgjDx7A99/DhAlw9iyUL29sKteiBTglZ+RZCOFoyd47xh5bgjFKKTUaeBkjEQkFjmutoxM8WQgh0kJEBCxeDF9/DRcvQpUqMGMGNG4M9qewCSEyiBTtHWNbphvo4FhEBtOunVEc19vb2+JIhLDj3j349luYPBmuXIG33zaSkTp1JPkQIpNIURKilKoOVADyYPSEHNBa/+nIwIT1PD1l5r7IgO7cgTlzYNo0uHEDatWCH3+EGjUk+RAik0lWEqKUqoZRtj12/2oFaNtnJ4GPtNa7HBqhsMyIESMSbyREerl1C2bONIZaQkKgQQMYMQLeesvqyIQQKZScDewqAVsxCpbtAHyBK0Ah4F3gHeA3pVR1rfXfjg9VCPFUun7d6PWYMwfu3oVmzYwJp5WfmOMmhMhkktMTMs7WvpnWelO8z0YrpZoBa2ztGjgoPmGhNm3aALBy5UqLIxFPpcuXYcoUmD/fmHz6wQcwbBiUK2d1ZEIIB0lOElIVWGcnAQFAa71BKfUzsoFdluHl5WV1COJpdOGCsdJl0SKIioK2bY3k45VXrI5MCOFgyUlCYoBTibQ5CdRNeTgiIxkyRIrfinR05oxR4+O774z3HTvCkCHw4ovWxiWESDPJSUL8gfKJtCkP7E15OEKIp87x40by8cMPRkXT7t3hs8/g+eetjkwIkcaSk4QMB3yVUr201vPif6iU6gO8B9R0UGzCYi1btgRg7dq1FkcisqR//jH2dfnpJ3Bzg/79YdAgKFLE6siEEOnENAlRSo20c3g7MFspNQD4E7gKPAe8DZQGtmAMx+xxfKgivb0lSx9FWvj7bxgzBtavh5w5jV6PTz6BZ5+1OjIhRDoz3TtGKRWTwmtqrbVz4s2eTrJ3jHhq/fUXjB0L//sf5MkDH39s/Mqf3+rIhBBpLCV7x7ybhvEIIZ4Wf/xh9Hz4+ECBAkYi0revkYgIIZ5qpkmI1npHegYiMp6mTZsCsHHjRosjEZmO1kbSMWYM/PknPPecscdLz57GEIwQQpDCvWPE0+G9996zOgSR2WhtDLeMHQt79kDRokap9a5dwd3d6uiEEBmMJCHC1Mcff2x1CCKziIkxJpqOHQsHDkCJEsYOtx07gqur1dEJITIop+Q0VkoVVkrNUUqdUkpFKKWi7fyKSqtghRAZTHS0sYNtuXLQsiWEhcHSpXDihFHvQxIQIUQCkrOBXVGMQmTPAUcAV+Ac8AAoZbtWABDq+DCFFRo0MLYA+uWXXyyORGQ4kZFGcbEJE4yE47XXYMUKY38XZ1kcJ4RImuQMx4zE2DG3ntbax7aEd6nW+iulVDFgIVACo2CZyAKaNGlidQgio3nwAJYtg4kTISgIvLxg7Vpo3hycktWxKoQQyUpC6gFbtNY+8T/QWl9USr0PHAZGA/0dFJ+wUO/eva0OQWQUERHGhnKTJsHFi/D66zBrFjRqBEpZHZ0QIpNKzo8uhTCGYWJFA4+mu2utw4CtQDPHhCaEsFxYGEyZAiVLGmXVS5SA334zCo81biwJiBAiVZLTE3IHyB7n/W2gaLw2ocAzqQ1KZAy1a9cGwMfnic4vkdWFhsKcOTBtGty8Ce+9B6tWQY0aVkcmhMhCkpOEnAOKx3l/EKillPLQWocrpZww9o256MgAhXVat25tdQgivd26BTNmGLU9QkKgQQMYMQJkHyEhRBpIznDMNuBdpZSL7f13QBHATyk1GdgFvAascmyI5pRSpZVSnyultiulLiilHiqlriqlNiilEiw7r5TqqJTaq5QKU0qFKqV8lVKNE2jvrpQarZQKVErdV0pdU0r9pJR61fFfWcbQrVs3unXrZnUYIj1cuwZDhsALL8BXX0HNmuDvD5s3SwIihEgzyekJWYwxBFMQuKy19lZKVQL6AeVsbVYC4xwbYoLGAK2Bo8Bm4BbgCTQFmiqlPtZaz4x/klJqCvApRq/NQoxhpjbAJqVUP6317HjtXTHmu1QD/IEZGL1C7wONlFK1tNayc7DIfC5fNsqpz58P9+8bS2y/+ALKlrU6MiHEU8B0F90kX0CpZzDqhARpra86JKqk37sTcFBrfSDe8RoYSYMGSmitL8f5rCpGr81poIrW+rbteAlgP5ADeEVrHRTnnKHAeGAN0FprHWM73gxYj5EElY09npDMtItuzZo1AfD19bU0DpEGzp83VrosWgRRUfDhhzBsGHh6Wh2ZECILMttFN9UL+7XW17XWe9I7AbHde1n8BMR2fAfgi9HDUTXexz1tr+NiExDbOUHAHIwibJ1jjyulVJxzPoubaGitNwB/Av8CstyMvU6dOtGpUyerwxCOdOYMdOsGL70ECxZA+/ZGsbHvvpMERAiR7rJydaFI22v8MvK1bK9b7JzzS7w2AC8CzwMntNZnk3hOliBJSBZy/Dh06AAvvwzLlxsl1U+dgoULoVQpq6MTQjylTOeEKKWWpPCaWmv9UQrPdQil1AsYlVvDgT/iHM+Bsaw4LO4QTRwnba8vxzkW++PhCZPb2Tsnfjzdge4Azz//fGLhZxiRkUYe5+LikkhLkWH984+xqdzq1cYutv37w+DBULiw1ZEJIUSCE1M7pfCaGrAsCbFNIv0BY1jls7hDLkAe26vZ/jaxx/Om8pzHaK0XAAvAmBNi1i6jqVOnDiBzQjKl/fuN5GP9esiVy1j5MnAgPCNlfIQQGUdCSUjJ9AhAKRUEvJCMU37QWrczuZYzsBxjFcsqYEoKw0pOohBbMjLTJBdJ1bVrV6tDEMm1ezeMGQO//AJ588KXXxq9H/nzWx2ZEEI8wTQJ0VqfS6cYTgP3k9H+kr2DtgTEG2PZ7E9AO/3k0p/YXos82Gev1yOxc3LbOSdLaNfObq4nMhqtYccOI/nYvh0KFoTx46FPH8idO/HzhRDCIsmpE5ImtNap3nVXKZUNWIGRgKwAOmito+3c655SKhgoqpQqbGdeSGnba9z5H4G2V7M5H/bOyRLCw8MB8PDwsDgSYZfWsHWrkXzs3AmFChn7vPTsCTlyWB2dEEIkKtOvjlFKZceo3/E+8D3Q3l4CEsd222t9O581iNcGjJ6a88DLSil7Q1T2zskSGjZsSMOGDa0OQ8SnNWzaBG++CfXqQVCQsaPtmTPw6aeSgAghMo1MnYTYJqH+jLFz72KgcxIKhs23vX6hlMoX51olgD7AA2Bp7HHbkE7sOZNse+TEntMMqI5RrGxHar6WjKhXr1706tXL6jBErJgYWLMGKlSApk3h+nWj1sepU9C3r7H6RQghMhHLh2NSaT7QELgBBAMj1ZNbi/tqrX1j32it/ZRS04BPgENKqTUYRc1aA/mBfnGrpdpMAxoDrYA9SqltGLVD3sdYBtwlKdVSMxvZwC6DiIqCn36CcePg6FGj1seyZdC2LcjyaSFEJpbZk5DY4ZGCwMgE2vnGfaO1/lQpdQjoi1G/Iwb4G5istf5v/JO11g+UUrWBIUBbYCBwB6Nk+5da66Op/DoypNBQY65tnjxmc3JFmoqMBG9vY5LpqVPw2mvw44/w/vvg7Gx1dEIIkWqp3jtGJI/sHSMS9eCB0dMxcaIx36NCBRg+HJo3B6dMPYIqhHhKme0dk9l7QkQa6t+/v9UhPF0iIowy6pMmQXAwvPEGzJ4NDRvCk8OMQgiR6UkSIky1aNHC6hCeDmFhMH++sbz26lWoXh2WLoXatSX5EEJkaZKECFM3btwAoGDBghZHkkWFhho9Hd98AzdvGknHTz/BO+9YHZkQQqQLSUKEqVatWgEyJ8Thbt2C6dNh5kwjEWnUyJjz8eabVkcmhBDpSpIQYerTTz+1OoSs5do1mDYN5swxhmD+/W8j+ahY0erIhBDCEpKECFNNmjSxOoSs4dIlmDwZvv0W7t+H1q3hiy+gTBmrIxNCCEtJEiJMXblyBYBChQpZHEkmde4cfP01LF4M0dHQrh0MHQqenlZHJoQQGYIkIcJUmzZtAJkTkmynThk1Pr77zljd0rkzfP45lCpldWRCCJGhSBIiTA0ZMsTqEDKXY8eM6qYrVkD27MZutp99BsWLWx2ZEEJkSJKECFP169vbaFg84dAhGDvW2FzO3R0GDoRBg0CGsYQQIkGShAhTFy5cAKC4/CRvn7+/kXxs2AC5csGQIUYC8swzVkcmhBCZgiQhwlT79u0BmRPyBD8/GDMGtmyBvHnhyy+hf3/In9/qyIQQIlORJESYGj58uNUhZBxaw44dRvKxfTsULGjM/+jTB3Lntjo6IYTIlCQJEaZq165tdQjW0xp++80Ydtm505jnMXUq9OgBOXJYHZ0QQmRqkoQIU2fOnAGg1NO4tFRr+O9/jZ6PffugWDGYNQs++siYfCqEECLVJAkRprp06QI8ZXNCYmJg3ToYNw4CAqBkSViwADp2NJbdCiEsExkZycWLF7l//77VoQgTbm5uFCtWDBcXlyS1lyREmBo9erTVIaSfqChYtcpIPo4dg5dfNoqNtW0L2eS/iRAZwcWLF8mVKxclSpRAKWV1OCIerTU3b97k4sWLlCxZMknnyHdXYapGjRpWh5D2IiNh+XKYMMGodFqmDKxcCa1agbOz1dEJIeK4f/++JCAZmFKKAgUKcP369SSfI0mIMBUYGAiAZ1bc6+TBA1i61Civfu4cVKhgDMM0awZOTlZHJ4QwIQlIxpbcvx9JQoSpHj16AFlsTkh4OCxcaOxqGxwMb74Jc+dCgwbGPi9CCCHSjSQhwtT48eOtDsFxwsJg3jyYMgWuXYMaNYw5H7VqSfIhhEgX8+fPx8PDgw4dOpi2CQgI4NKlSzRs2DAdI7Pu/pKECFNVq1a1OoTUCw01ltZ+8w3cugV168Lw4VC9utWRCSGeMj179ky0TUBAAP7+/slKAqKiosjmoAn0Kbl/akgSIkwdPnwYgDJlylgcSQrcvAnTpxsJSGgoNG5sJB9vvGF1ZEIIBxiwZQABVwIcek2vQl5Mrz/d9POgoCAaNGjA22+/jZ+fH0WLFmXDhg24u7sTEBBAz549CQ8P58UXX2TJkiXky5fvsfNHjRpFzpw5GTRoEDVr1uSNN97g999/JyQkhMWLF/PGG28wcuRIIiIi2LlzJ0OHDqVx48b069ePf/75h6ioKEaNGkWzZs1YtmwZ//vf/7h//z737t1j+/btTJo0ieXLl+Pk5ESDBg2YOHEip0+fpk+fPly/fh0PDw8WLlzIK6+8QqdOnXBzc+PIkSNcvXqVadOmUbdu3Sfu37p1a4f+GccnSYgw1bdvXyCTzQm5ehWmTTPmeYSFQYsWRvJRoYLVkQkhsoCTJ0/y448/snDhQj744APWrl1Lu3bt6NChA7NmzaJGjRqMHDmS0aNHM326eUIDRg/G3r172bx5M6NHj8bHx4evvvoKf39/Zs+eDcCwYcOoVasWS5YsISQkhNdff/1RNevdu3dz6NAh8ufPzy+//ML69evZs2cPHh4e3Lp1C4Du3bszf/58SpcuzZ49e+jduzfbt28HjKRqx44dnD59mnfffZdTp049cf+0JkmIMDV58mSrQ0i64GBjsumCBcbKl9at4Ysv4LXXrI5MCJEGEuqxSEslS5bEy8sLgEqVKhEUFERoaCghISGPyhp07NiR999/P9FrtWjR4rHr2PPbb7+xceNGpkyZAhjLlM+fPw9AnTp1yG/bONPHx4fOnTvj4eEBQP78+QkLC8PPz++xWB48ePDo9x988AFOTk6ULl2aUqVKcfz48eT8UTiEJCHCVJUqVawOIXHnzsHXX8PixRAdDe3bw9ChRrExIYRwMFdX10e/d3Z2JiIiItXXcnZ2Jioqym4brTVr1659olTCnj17yBFn/yqt9RPLY2NiYsibNy8BAfaHreK3t2L5sxREEKYCAgJM//Fa7tQpYx+Xl16CRYugUyc4edKo/SEJiBAiHeXJk4d8+fLx559/ArB8+fIUF3vMlSsXd+/effS+Xr16zJo1C601AAcOHLB7Xt26dVmyZAnh4eEA3Lp1i9y5c1OyZElWr14NGInKwYMHH52zevVqYmJiOH36NGfOnMHT0/OJ+6c1SUKEqQEDBjBgwACrw3jcsWNGb4enJ6xYAb16wenT8O23xj4vQghhge+++47BgwdTrlw5AgICGDlyZIqu8+6773L06FG8vLxYtWoVI0aMIDIyknLlylGmTBlGjBhh97z69evTtGlTKleujJeX16Phmx9++IHFixdTvnx5XnvtNTZs2PDoHE9PT2rUqEGDBg2YP38+bm5uT9w/ranY7Eqkj8qVK2t/f3+rw0iS2F6Q2PFPSx08aOzrsmaNsYttr14waBAUKmR1ZEKIdHLs2DFeffVVq8PIEjp16kTjxo1p1aqVw69t7+9JKbVfa105fluZEyJMZYjkw98fxoyBjRshVy5jvsfAgVCwoNWRCSGESCVJQoSpffv2ARZNUN21C8aOhS1bIF8+GD0a+vUzfi+EECJVli1bZnUIgCQhIgGDBw8G0rFOiNbg62v0fPz+u9HbMWEC9O4NuXOnTwxCCCHSjSQhwlR6FatBa/j1VyP58PODwoWNgmPdu0OcJWhCCCGyFklChKk0L9eutTHXY+xYY+5H8eIwZw506QJubml7byGEEJaTJbrClJ+fH35+fo6/cHQ0rF4NXl7QvLmxsdzChUbtj969JQERQoinhCQhwtSwYcMYNmyY4y4YFQXe3lC2LHzwgVFe/fvvITAQunaF7Nkddy8hhLBAw4YNCQkJISQkhLlz5z467uvrS+PGjRM9f+TIkfj4+CTYxtfXN21+QEwiR95fhmOEqW+//dYxF3r4EJYvNyaZnj4NZcrAypXQqhU4OzvmHkIIkQFs3rwZMDaHmzt3Lr17907W+V999VWibXx9fcmZMydVq1ZN8nWjoqLIls0xj/yU3N+MJCHCVPy9CpLt/n1YssTY2+X8eahYEX7+GZo2BSfphBNCpMKAAeDobSW8vCCBnW8nTZqEm5sb/fv3Z+DAgRw8eJDt27ezbds2li5dire3NyVKlMDf358hQ4Zw+vRpvLy8qFOnDo0aNSIsLIxWrVpx+PBhKlWqhLe39xP7tcQtIlaiRAk6duzIpk2biIyMZPXq1bi5uTF//nycnZ3x9vZm1qxZvPLKK/Ts2fPRxnbTp0+nWrVqjBo1ikuXLhEUFETBggVZvnw5n3/+Ob/++itKKbp160a/fv3Yv38/n3zyCWFhYRQsWJBly5ZRuHBhatasiZeXF3v37uXOnTssWbKEZ5999on7V69ePcV/5JKECFM7duwASP4eCOHhxm62kyfDpUvw5pswbx40aAAWbJAkhBCO8M477zB16lT69++Pv78/Dx48IDIykp07dz7xIJ44cSKHDx9+VHna19eXAwcOcOTIEYoUKUK1atXYtWsXb7/9doL3LFiwIH///Tdz585lypQpLFq0iJ49e5IzZ04GDRoEQNu2bRk4cCBvv/0258+fp169ehw7dgyA/fv3s3PnTtzd3Zk3bx5nz57lwIEDZMuWjVu3bhEZGUm/fv3YsGEDzzzzDKtWreKLL75gyZIlANy7dw8/Pz/++OMPunTpwuHDh5+4f2pIEiJMffnll0Ay6oTcvWskG1OnwrVrUKOGMeejVi1JPoQQjpVAj0VaqVSpEvv37+fu3bu4urpSsWJF/P39+fPPP5k5c2ai57/++usUK1YMMCpSBwUFJZqEtGjR4tG9161bZ7eNj48PR48effT+zp07jzaha9q0Ke7u7o/a9ezZ89GwTP78+Tl8+DCHDx+mTp06AERHR1O4cOFH1/rPf/4DGAnYnTt3CAkJSfTrTA5JQoSp2Ew4USEhMHs2fPONsdKlbl0YPhxS0UUnhBAZjYuLCyVKlGDp0qVUrVqVcuXK8fvvv3P69Okk7Wnj6ur66PfOzs5ERUUl+ZyE2sfExLB79+5HyUZcOeLUWtJaPzH8o7XmtddeY/fu3XavHb99/PepJQPzwlSpUqUoVaqUeYObN2HECHjhBeO1WjXYs8coPCYJiBAiC3rnnXeYMmUK77zzDtWrV2f+/Pl4eXk98XDOlSvXo94IR4t/7bp16z5WXDLAZK5M3bp1mT9//qNk5tatW3h6enL9+vVHSUhkZCRHjhx5dE7sTro7d+4kT5485MmTx6FfmyQhwpSPj4/9pWJXr8JnnxnJx9ixUKcOHDhgFB57/fX0D1QIIdJJ9erVuXz5Mm+99RbPPfccbm5udidmFihQgGrVqlGmTJlHW2A4SpMmTfj555/x8vJ6NBTk7+9PuXLl+Ne//sX8+fPtnte1a1eef/55ypUrR/ny5VmxYgXZs2dnzZo1fP7555QvXx4vL6/Hlt/my5ePqlWr0rNnTxYvXmz3/qmhtNapuoBInsqVK2t/f3+rw0iSmjVrAnHmhAQHw6RJxqTThw+hTRsYNgxee82yGIUQTw97W8SLtFOzZk2mTJlC5cqVk3Wevb8npdR+rfUTF8pyPSFKqcVKKW379VIC7ToqpfYqpcKUUqFKKV+llGklGaWUu1JqtFIqUCl1Xyl1TSn1k1Iqy/6PWL58OcuXL4egIOjVC0qVgrlzjeTj+HH44QdJQIQQQqRYlpqYqpRqAnQBwoCcCbSbAnwKXAQWAtmBNsAmpVQ/rfXseO1dga1ANcAfmAEUB94HGimlammt9zj+K7JW8QcPYPx4o9CYkxN07gxDhkCJElaHJoQQIo2lxw7qWSYJUUo9g5FQrAIKAXaLWyilqmIkIKeBKlrr27bjk4H9wBSl1H+11kFxTvsEIwFZA7TWWsfYzlkFrAeWKKXKxh7P9I4ehfHj2bJiBWTLRv3evWHwYLAtLRNCCCEcISsNxyywvfZJpF1P2+u42AQEwJZ0zAFcgc6xx5Ux5Tn2nM/iJhpa6w3An8C/MEl6MpWDB+H9942y6uvXM7FYMSZWrgwzZkgCIoQQwuGyRBKilOoENAd6aq1vJtK8lu11i53PfonXBuBF4HnghNb6bBLPyVz27jVKqXt5wW+/GZNNg4JYuXcvK02K4wghhBCplemHY5RSL2DM0fDWWq9PpG0OoCgQprW+bKfJSdvry3GOxW6gcsLksvbOyRx27oQxY4zEI18++Oor6NcP8uYFjDEtIYQQIq1k6p4QpZQT8B3GRNT+STglj+011OTz2ON5U3nOY5RS3ZVS/kop/+vXrychzDSkNWzfDu++axQUO3AAJk6Ec+eMgmN5///L2LRpE5s2bbIwWCGEyLhGjRrFlClTEmyzfv36x0qqp4VLly7RqlWrRNuNHz8+TeNICcuTEKVUUJwltUn55R3n9IEYczG6xZ3f4QDJKZ4SWybP9Byt9QKtdWWtdeVnnnkmdZGllNbwyy/w9tvw3nsQGGiUWT97Fj7/HHLleuKUqVOnMnXqVAuCFUKI1Ft/IJhqE7dTcsj/qDZxO+sPBKd/DOmQhBQpUoQ1a9Yk2k6SEPtOA4HJ+HUJQClVGhgHLNVab07ivWJ7LfKYfG6v1yOxc3LbOSfjiImB9euhShVo2BAuXIA5c+DMGWMr7Dj7CsS3x1+gpgAAEI1JREFUZs2aJP3DFkKIjGb9gWCGrvuH4JAINBAcEsHQdf+kOhEZN24cnp6e1K5dm8DAwEfHFy5cSJUqVShfvjwtW7YkPDwcPz8/Nm7cyODBg/Hy8uL06dN228U3atQo2rdvT61atShdujQLFy4EjH1eBg8eTJkyZShbtuyjkur/197dR0lRnXkc//4cRwZiBJVEJSSoWcBVRhCJRhJQ4yawi0HxqOBKCK7iZk+Cm2xQcaNh1M2Jq+dogqjra+YkaPBEVLKRje8IRtQAjkjMGjcRXF8D40IIDvLis39U9aRpupmeYXpqZvh9zqlT9L23qp+eoqafqbp17+rVqxkyZAgA9fX1nHHGGYwdO5aBAwdyySWXADBz5kyampoYNmwY5557Lps2bWLcuHEMHTqUIUOGNO+ro2XeJyQiTmnjpkeRPski6bwSbV5Nx/OfEBEPRsQmSW8Cn5B0SJF+IQPTdX7/j9z/slJ9Poptk73t22H+/GRY9ZdeSgYau+MO+MpXYJ99ytpF3759KxykmVllXPfwKzRt3b5DWdPW7Vz38Cucfswn2rTP5cuXM2/ePF544QW2bdvG8OHDOfbYY4Fktttp06YBcPnll3PnnXcyffp0xo8fz6mnntp8u6RPnz5F2xVauXIlzz77LJs2beKYY45h3LhxLF26lIaGBl588UXWrVvHZz7zGUaPHr3Ttg0NDbzwwgv06NGDwYMHM336dK655hrmzJnTPK/M/Pnz6devHw899BAAGzZk83d0Z7gS0largTtLLO+kbX6Wvl6dt90T6XpskX3+bUEbSK7UvA4MknRYmdtkZ9u2ZHCxIUNg4kTYuhV+/OPk9sv555edgADcf//9JaeONjPrzN5a39Sq8nIsWbKECRMm0KtXL/bbbz/Gjx/fXLdq1SpGjRpFbW0td9999w6TwOUrt91pp51Gz5496du3LyeffDLPP/88Tz/9NOeccw5VVVUcdNBBnHjiifz617/eadtTTjmF3r17U1NTw5FHHsmaNWt2alNbW8tjjz3GpZdeypIlS+jdu9TF/srqsklIRDRExAXFFv5y9eJf07L8KQVzM/t8R9L+uUJJh5KMMfIB8KO894m8ba5NO8PmtjkNGAW8DDzVvp+wlbZsgTvvhCOOgClToLoa7r0XVq1Krn7s3fqLXrNnz2b27NkVCNbMrLL69dl5WvtdlZer1FT2U6dOZc6cObz00kvMmjWLzZs371a7wveRRLlzvfXo0aP531VVVc2z5uYbNGgQy5cvp7a2lssuu4yrrrqqrH23ty6bhLRVRDwDXE8y/sdKSTdIuolkOPYDgBkFo6WStn8GOBN4TtI1ku4hGUH1feAfMhstdfPmZD6XgQPhgguSp1sefBAaGuDss6Gqqs27XrBgAQsWLGjHYM3MOsbFYwbTs3rH3389q6u4eMzgElu0bPTo0TzwwAM0NTWxcePGHZ4e3LhxI4cccghbt27l7rvvbi4vnPa+VLtCCxYsYPPmzTQ2NrJo0aLmWy/33nsv27dvZ+3atSxevJjjWjFzeXV1NVu3bgWSJ2p69erF5MmTmTFjBitWrGjNj6LdZN4nJAsR8W1JK4FvABcCHwIrgOsi4hdF2n8g6W+AmcDfkzyV8yeSIdtnRURluz6XsnhxMpnc22/DyJFw660wZgyUyNRbK6vLc2ZmuyvX7+O6h1/hrfVN9OvTk4vHDG5zfxCA4cOHM3HiRIYNG8aAAQMYNWpUc93VV1/N8ccfz4ABA6itrW1OPCZNmsS0adOYPXs29913X8l2hY477jjGjRvH66+/zhVXXEG/fv2YMGECS5cuZejQoUji2muv5eCDD2b16tVlxX/hhRdy9NFHM3z4cKZMmcLFF1/MXnvtRXV1Nbfcckubfy67Q+Ve3rH2MWLEiFi2bFn77Oztt5NJ5S65JBn3o52Sj5xcb+mJEye2637NzNqi2BTx3VFdXR377rsvM2bMyDqUNil2nCQtj4gRhW33yCsh3cYhh8Avi40+3z5ymbGTEDMzqwQnIVbSwoXlDr9iZmbtpa6uLusQOoyTECupV69eWYdgZraDiCj5hIplr7VdPPa4p2OsfHPnzmXu3LktNzQz6wA1NTU0Nja2+ovOOkZE0NjYSE1NTdnb+EqIlXTHHXcAMHny5IwjMTOD/v3788Ybb5D5RKBWUk1NDf379y+7vZMQK+nRRx/NOgQzs2bV1dUcdlixgautq3ISYiVVV1dnHYKZmXVj7hNiJdXX11NfX591GGZm1k05CbGSnISYmVklecTUDiZpLbDzlIadV19gXdZBWMX4+HZfPrbdV1c8tgMi4mOFhU5CbJckLSs21K51Dz6+3ZePbffVnY6tb8eYmZlZJpyEmJmZWSachFhLbss6AKsoH9/uy8e2++o2x9Z9QszMzCwTvhJiZmZmmXASYmZmZplwEmJmZmaZcBJiO5HUX9Jdkt6S9IGk1ZJ+IGn/rGOz3ZMeyyixvJN1fNYySWdKulHSEkl/So/d3Ba2GSlpoaT3JL0vaaWkb0qq6qi4rWWtObaSDt3FuRyS5nV0/G3hCexsB5I+DTwDfBxYAPw3cBzwz8BYSZ+LiMYMQ7TdtwH4QZHyP3d0INYmlwNDSY7XG8ARu2os6TRgPrAZuBd4D/gycAPwOeCsSgZrrdKqY5t6EXiwSPmqdoyrYpyEWKGbSRKQiyLixlyhpOuBbwHfA76WUWzWPtZHRF3WQVibfYvkC+p/gBOBJ0s1lLQfcDuwHTgpIpal5VcATwBnSpoUEV3ir+Y9QNnHNk9DVz6ffTvGmkk6HPgSsBq4qaB6FrAJ+Iqkj3RwaGaWiognI+LVKG98hTOBjwHzcglIuo/NJH91A/xTBcK0Nmjlse0WfCXE8n0hXT8SER/mV0TERkm/IklSPgs83tHBWbvpIWky8CmSxHIlsDgitmcbllVA7pz+ZZG6xcD7wEhJPSLig44Ly9pRP0n/CBwINAJLI2JlxjGVzUmI5Rucrn9Xov5VkiRkEE5CurKDgZ8UlL0m6byIeCqLgKxiSp7TEbFN0mvAUcDhwG87MjBrN19Ml2aSFgFfjYjXM4moFXw7xvL1TtcbStTnyvt0QCxWGT8CTiFJRD4C1AK3AocC/yVpaHahWQX4nO6+3geuBo4F9k+XXD+Sk4DHu8Ktcych1hpK13vM/cruJiKujIgnIuLdiHg/IlZFxNeA64GeQF22EVoH8zndRUXEHyPiuxGxIiLWp8tikqvVzwF/BVyQbZQtcxJi+XJ/FfUuUb9fQTvrPv4jXY/ONAprbz6n9zARsQ24I33Z6c9nJyGW75V0PahE/cB0XarPiHVdf0zXnf7yrbVKyXNa0t7AYcA24A8dGZRV3Np03enPZychli/3TPqXJO3wf0PSR0kGNmoCnu3owKziTkjX/jLqXp5I12OL1I0GegHP+MmYbuez6brTn89OQqxZRPweeISkk+LXC6qvJMmqfxwRmzo4NGsHko6SdECR8gHAnPTlLof/ti7nPmAdMEnSiFyhpBrg39KXt2QRmO0eScdL2qdI+RdIBj2DLnA+aw8aE8XKUGTY9t8CxwMnk9yGGelh27smSXXATJIrXq8BG4FPA+OAGmAhMCEitmQVo7VM0unA6enLg4ExJH/xLknL1kXEjIL295EM2z6PZNj28SSP794HnL0nDY7VmbXm2KaP4R4FLCIZZRXgaP4yNswVEZFLNDstJyG2E0mfBK4iuYR7IPA2ydwEV0bEe1nGZm0n6USSIfeP4S+P6K4HGkjGDfmJv4w6vzSZnLWLJmsi4tCCbT4HfIfktlsNybDgdwGzPUhd59GaYyvpfGACMAToC1QD7wJLgTkRsaTUTjoTJyFmZmaWCfcJMTMzs0w4CTEzM7NMOAkxMzOzTDgJMTMzs0w4CTEzM7NMOAkxMzOzTDgJMTMzs0w4CTGz3SLpIkkvS2qSFJK+mXVMZtY1OAkxszaTNAn4IcmQ4D8gmWOoQyY4lHRSmvTUdcT7mVn72zvrAMysSzs1t46ItzKNxMy6HF8JMbPd0Q/ACYiZtYWTEDNrNUl1koJkdmXS2yKRluW3O0JSvaT/lfSBpHcl3SNpcJF9DpJ0jaRlktam7ddIuk1S/4K29SSzAQPMyn9/SSflx5h7XbD9oWldfeF+0/LDJU2XtDLt67KooN0YSQslrUvj/L2k6yT1KfPnd5ik9ZLekzSgoO4jkn4raXs66aBZt+XbMWbWFovS9VRgAElfkB1IGgvcTzK753+SzNzaHzgDGCfp5IhYkbfJGSSz/D4JPANsIZmq/ALgy5JGRMSbadsH0/VXgafy4gFYvVufLPFDYBTwELAQaJ5pVtJ3ST7ve8AvgD+STKE+A/g7SSdExJ92tfOIeE3SBcDPgJ9KGh0R29Lqm4EjgLqIeKodPotZ5xURXrx48dKmheTLP4qU7w/8H7AOOLKg7ijgz8CKgvJPAD2K7OtLJEnALQXlJwFB8mVdLLa6tP6kInWHpnX1BeX1afmbwGFFtjs5rX8G6FNQNzWtu6EVP7+b022+n76ekr5+Etgr6+PrxUulF9+OMbNKmAL0AWZFxMv5FRHxG+B24BhJR+aVvxkRHxTuKCIeAX4DjKlsyDu4NiJeK1J+UbqeFhHr8ysioh5oAM5txfv8C/AicKmkb5AkJWuBcyPiw1ZHbdbF+HaMmVXCCel6aIlHaAel678GXgaQJJIv8KnAUJKrKVV522ypRKAlPF+i/ARgK3CWpLOK1O8DfEzSgRHR2NKbRMRmSROBZcCNJFdBzgx39LU9hJMQM6uEA9P1tBba7Zv37+uBbwJvAw+T3BJpSuumkvQ96SjvlCg/kOT35qwWtt8XaDEJSf0OWAmMJEnIHilzO7Muz0mImVXChnQ9NCJWttRY0sdJbnWsAkZGxMaC+nPaEEPudkax33MtPcUSJco3kPTVOKAN8ZQykyQBWUfSX+Yy4HvtuH+zTst9QsysEnKjpo4qs/3hJL+PHimSgPRP6wvlnlipKlIHScdYgE8WqRtRZlyFngX2l3RUG7ffgaSRwFXAK8CQdH2lpM+3x/7NOjsnIWZWCT8C1pOM4XFcYaWkvQrG71idrj8vqSqv3b4knViLXc3I3e74VIkYcv06zpPUvL2kTwLfLeMzFHNDur5dUr/CynSMj8+WsyNJ+wM/JUmmJkXEu8BEYBvJY7sH7mp7s+7At2PMrN1FRKOkM4EHgGclPU7yhMuHJEnDCST9K2rS9u9ImgdMAhokPQL0Br5IMi9NAzCs4G1eIek3MknSFuB1ktsoP4mINRHxnKTFwGjgeUlPAAcBXybpc1LsCklLn+txSTOB7wOvSloIvEbSB2QAcCLwNDC2jN3dlf4sLoqIhnT/L0r6NjCHJJEb39oYzboSJyFmVhHpF3ZuEK8xJLdmtgBvAU8A8ws2OR/4A8nVgK+TPKr6c5KrFoVtiYjtkiYA1wBnAx8FRJIErEmbnQZcl66nA68Cl5B0/jy7jZ/r3yX9iqQPy+fTfW8gSYhuA+5paR+SpgOnAz+PiBsL9n+TpFOACZK+FRE3FN2JWTegiFL9r8zMzMwqx31CzMzMLBNOQszMzCwTTkLMzMwsE05CzMzMLBNOQszMzCwTTkLMzMwsE05CzMzMLBNOQszMzCwTTkLMzMwsE/8PYdTRqzsOynAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model # import \"linear_model\" from sklearn\n",
+    "from sklearn.datasets import make_regression # import \"make_regression from sklearn.datasets\"\n",
+    "\n",
+    "# generate data using the imported \"make_regression\" function\n",
+    "syn_x, syn_y = make_regression(n_samples=30, n_features=1, noise=30, random_state=1) \n",
+    "syn_x = syn_x + 10*np.ones(30).reshape(syn_x.shape) # add a 10* identity matrix to data\n",
+    "\n",
+    "#print(syn_x, \"\\n\")\n",
+    "#print(syn_y, \"\\n\")\n",
+    "\n",
+    "# plot regression dataset\n",
+    "plt.rc('font', size=20) # change plot font\n",
+    "\n",
+    "### STUDENT TASK ###\n",
+    "# create Linear Regression model without an intercept\n",
+    "\n",
+    "# fit a linear regression model (without intercept)\n",
+    "# reg = ... \n",
+    "\n",
+    "# find the optimal weight for linear regression model (without intercept)\n",
+    "# w_opt = ...\n",
+    "\n",
+    "\n",
+    "#print(w_opt)\n",
+    "\n",
+    "# create Linear Regression model using an intercept term \n",
+    "# reg_intercept = ...\n",
+    "\n",
+    "# fit a linear regression model (with intercept)\n",
+    "# reg_intercept = ... \n",
+    "\n",
+    "# find the optimal weights for linear regression model (with intercept)\n",
+    "# w_opt_intercept = ...\n",
+    "\n",
+    "# find the intercept for linear regression model (with intercept)\n",
+    "# intercept = ...\n",
+    "\n",
+    "\n",
+    "# remove the line raise NotImplementedError() before testing your solution and submitting code\n",
+    "# YOUR CODE HERE\n",
+    "reg = linear_model.LinearRegression(fit_intercept=False)\n",
+    "\n",
+    "reg.fit(syn_x, syn_y)\n",
+    "\n",
+    "w_opt = reg.coef_[0]\n",
+    "\n",
+    "reg_intercept = linear_model.LinearRegression(fit_intercept=True)\n",
+    "\n",
+    "reg_intercept.fit(syn_x, syn_y)\n",
+    "\n",
+    "w_opt_intercept = reg_intercept.coef_[0]\n",
+    "\n",
+    "intercept = reg_intercept.intercept_\n",
+    "\n",
+    "\n",
+    "# print parameters of the optimal predictor \n",
+    "print(\"model without intercept: optimal weight w = {}\".format(w_opt))\n",
+    "print(\"model wit intercept: optimal weight w = {} and intercept = {}\".format(w_opt_intercept,intercept))\n",
+    "\n",
+    "# create a grid of test feature values  \n",
+    "x_grid = np.linspace(-1, 16, num=100).reshape(-1,1) \n",
+    "# compute predictions from linear regression model without intercept term \n",
+    "y_pred = reg.predict(x_grid) \n",
+    "# compute predictions on test feature values using linear regression model with intercept term \n",
+    "y_pred_intercept = reg_intercept.predict(x_grid)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 1, figsize=(8, 4)) # initialize subplots and get \"fig\" and \"axes\" variables\n",
+    "axes.scatter(syn_x, syn_y, label='data points') # create a scatter plot with the generated synthetic data\n",
+    "axes.plot(x_grid, y_pred, color='green', label='no intercept') # add a line to the plot\n",
+    "axes.plot(x_grid, y_pred_intercept, color='red', label='with intercept') # # add a line to the plot\n",
+    "\n",
+    "axes.legend() # add a legend to the plot\n",
+    "axes.set_xlabel(\"feature x\") # add x-axis label to the plot \n",
+    "axes.set_ylabel(\"label y\") # add y-axis label to the plot\n",
+    "axes.axhline(y=0, color='k',linestyle=':') # add a dotted lien to the plot\n",
+    "axes.axvline(x=0, color='k',linestyle=':') # add a dotted line to the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "d18b136c0ff9ededb750bf78d915dba3",
+     "grade": true,
+     "grade_id": "cell-f9495c305f7fa466",
+     "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 constains visible tests (sanity checks) and \n",
+    "# hidden tests which are used for grading student solutions \n",
+    "\n",
+    "assert w_opt < 1, \"w_opt value is wrong\"\n",
+    "assert w_opt_intercept < 50, \"w_opt_intercept value is wrong\"\n",
+    "assert intercept > -400, \"intercept value is wrong\"\n",
+    "\n",
+    "\n",
+    "print('Sanity check tests passed!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "e55e5729e2e1f1b9a4b85d0388d8d572",
+     "grade": false,
+     "grade_id": "cell-07143459df52a5c0",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "## Take Home Quiz "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "0a3bb9053c3a9b72b7282d5619152aa8",
+     "grade": false,
+     "grade_id": "cell-81144558954a5835",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "Answer the following questions by setting the `answer_R1_Q??` variable for each question to the number of the correct answer. For example, if you think that the second answer in the first quiz question is the right one, then set `answer_R1_Q1=2`. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "ddc3afd2fd00e7a59d4bc7bb8d03a506",
+     "grade": false,
+     "grade_id": "cell-1aa1ddb20cf88962",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='QuestionR1_1'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <b>Student Task.</b> Question R1.1. \n",
+    "\n",
+    "<p> Modify the code in the demo \"Fitting a Linear Model to Data\" to determine the difference (error) between the predicted value y_pred and the true label y for the first data point (which corresponds to the index 0). Select the correct value (rounded to one decimal) of this error below.</p>\n",
+    "\n",
+    "<ol>\n",
+    "  <li>4.6</li>\n",
+    "  <li>-3.8</li>\n",
+    "  <li>5.0</li>\n",
+    "  <li>7.5</li>\n",
+    "</ol> \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "2514fe10123725f50f059bc2b31ee4b2",
+     "grade": false,
+     "grade_id": "cell-be2c274c69a172dc",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# answer_Q1\n",
+    "\n",
+    "# remove the line raise NotImplementedError() before testing your solution and submitting code\n",
+    "\n",
+    "# answer_R1_Q1  = ...\n",
+    "\n",
+    "# YOUR CODE HERE\n",
+    "answer_R1_Q1  = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "6e200b29d86603858ff5b08cd813e331",
+     "grade": true,
+     "grade_id": "cell-a37329cc76fcdc18",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity check tests passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this cell is for tests\n",
+    "\n",
+    "assert answer_R1_Q1 in [1,2,3,4], '\"answer_R1_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": "4594f7699bd395bfe01b8281a5858964",
+     "grade": false,
+     "grade_id": "cell-3046aeea89e6dec3",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<a id='QuestionR1_2'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <b>Student Task.</b> Question R1.2. \n",
+    "\n",
+    "<p>Consider data points having features $\\mathbf{x}$ and a numeric label $y$. How can the quality of a predictor function $h(\\mathbf{x})$, which delivers a predicted label $\\hat{y}=h(\\mathbf{x})$ be measured to choose the best predictor function?</p>\n",
+    "\n",
+    "\n",
+    "<ol>\n",
+    "  <li>Use the difference between our predicted value $\\hat{y}$ and true label $y$, i.e: $y - \\hat{y}$ and pick the predictor function with the lowest value of loss.</li>\n",
+    "  <li>Use the squared error loss $(y - \\hat{y} )^{2}$ and pick the predictor function with the highest value of squared error loss.</li>\n",
+    "  <li>Use the squared error loss $(x - \\hat{x})^{2}$ and pick the predictor function with the lowest value of squared error loss.</li>\n",
+    "  <li>Use the squared error loss $(y - \\hat{y})^{2}$ and pick the predictor function with the lowest value of the squared error loss.</li>\n",
+    "</ol> \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "c2b15f2a3be7989d265d93621ee1b01a",
+     "grade": false,
+     "grade_id": "cell-8b0c045a8dd164b0",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# answer_Q2\n",
+    "\n",
+    "\n",
+    "\n",
+    "# remove the line raise NotImplementedError() before testing your solution and submitting code\n",
+    "# answer_R1_Q2  = ...\n",
+    "\n",
+    "# YOUR CODE HERE\n",
+    "answer_R1_Q2  = 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "25898bfdd32855a647ccc4395f874d56",
+     "grade": true,
+     "grade_id": "cell-f8cf4a0b2c7d28bd",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity check tests passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this cell is for tests\n",
+    "\n",
+    "assert answer_R1_Q2 in [1,2,3,4], '\"answer_R1_Q2\" 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": "e644c05221b64bddf41f83fabc59064e",
+     "grade": false,
+     "grade_id": "cell-790a88a704d347ee",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "source": [
+    "<a id='QuestionR1_3'></a>\n",
+    "<div class=\" alert alert-warning\">\n",
+    "    <b>Student Task.</b> Question R1.3. \n",
+    "\n",
+    "<p> Consider a set of $m=3$ data points represented by the feature vectors $\\mathbf{x}^{(1)}=\\big(1,0,0,0\\big)^{T}$, $\\mathbf{x}^{(2)}=\\big(1,0,1,0\\big)^{T}$ and $\\mathbf{x}^{(3)}=\\big(1,0,-1,0\\big)^{T}$. What is $x^{(2)}_{3}$ ? </p>\n",
+    "\n",
+    "<ol>\n",
+    "  <li>1 </li>\n",
+    "  <li>0</li>\n",
+    "  <li>-1</li>\n",
+    "</ol> \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "4008f2d2b672d10738d32f71fa61622a",
+     "grade": false,
+     "grade_id": "cell-91248121300e8eb8",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# answer_Q3\n",
+    "\n",
+    "# answer_R1_Q3  = ...\n",
+    "# remove the line raise NotImplementedError() before testing your solution and submitting code\n",
+    "# YOUR CODE HERE\n",
+    "answer_R1_Q3  = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "7b585f7e31546d9e7147c81002eedf28",
+     "grade": true,
+     "grade_id": "cell-73b6333b16763e07",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity check tests passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this cell is for tests\n",
+    "\n",
+    "assert answer_R1_Q3 in [1,2,3], '\"answer_R1_Q3\" 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": "304.8px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
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+    "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
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}