diff --git a/01_introduction.ipynb b/01_introduction.ipynb
index 1583f89af9173904297cd29bcd3df0df4e27aa35..4fbfa67def684e491e554c97d7dd4944e6516abe 100644
--- a/01_introduction.ipynb
+++ b/01_introduction.ipynb
@@ -4,103 +4,121 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter 1: General Introduction"
+    "# Chapter 1: General Introduction to machine learning (ML)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## What is machine learning ?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Discipline in the overlap of computer science and statistics\n",
-    "- Subset of Artificial Intelligence (AI)\n",
-    "- Term \"Machine Learning\" was first used in 1959 by AI pioneer Arthur Samuel\n",
+    "## ML = \"learning models from data\"\n",
     "\n",
-    "- **Learn models from data**\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So the field is not as new as one might think, but due to more available data, processing power and development of better algorithms more applications of machine learning appeared during the last 15 years."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## About  models\n",
     "\n",
-    "Model examples: \n",
+    "### About models\n",
     "\n",
-    "   1. Where will my car stop when I break now ?\n",
+    "A \"model\" allows us to explain observations and to answer questions. For example:\n",
+    "\n",
+    "   1. Where will my car at given velocity stop when I break now ?\n",
     "   2. Where on the night sky will I see the moon tonight ?\n",
     "   2. Is the email I received spam ? \n",
     "   4. What article X should I recommend to my customers Y ?\n",
     "   \n",
-    "The first two questions can be answered based on existing mathematically explicit models (formulas). \n",
+    "- The first two questions can be answered based on existing physical models (formulas). \n",
+    "\n",
+    "- For the  questions 3 and 4 it is difficult to develop explicitly formulated models. \n",
     "\n",
-    "For the  questions 3 and 4 it is difficult to develop explicitly formulated models. \n",
+    "### What is needed to apply ML ?\n",
+    "\n",
+    "Problems 3 and 4 have the following in common:\n",
+    "\n",
+    "- No exact model known or implementable because we have a vague understanding of the problem domain.\n",
+    "- But enough data with sufficient and implicit information is available.\n",
     "\n",
-    "These problems 3 and 4 have the following in common:\n",
     "\n",
-    "- No exact model known or implementable\n",
-    "- Vague understanding of the problem domain\n",
-    "- Enough data with sufficient (implicit) information available\n",
     "\n",
     "E.g. for the spamming example:\n",
     "\n",
     "- We have no explicit formula for such a task\n",
-    "- We know that specific words are specific for spam emails, other words are specific for my personal and job emails.\n",
+    "- We have a vague understanding of the problem domeani, because we know that some words are specific for spam emails, other words are specific for my personal and job emails.\n",
     "- My mailbox is full with examples for spam vs non-spam.\n",
     "\n",
     "\n",
-    "**In such cases machine learning offers approaches to build models based on example data.**\n"
+    "**In such cases machine learning offers approaches to build models based on example data.**\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "## ML: what is \"learning\" ?\n",
+    "\n",
+    "To create a predictive model, we first must \"learn\" such a model on given data. \n",
+    "\n",
+    "All ML algorithms have in common that they rely on internal data structures and/or parameters. Learning then builds up such data structures or adjusts parameters based on the given data. After that such models can be used to explain observations or to answer questions.\n",
+    "\n",
+    "The important difference between explicit models and models learned from data:\n",
+    "\n",
+    "- Explicit models usually offer exact answers to questions\n",
+    "- Models we learn from data usually come with inherent uncertainty."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "\n",
     "## Some history\n",
     "\n",
-    "Rough time line with a few examples\n",
+    "Some parts of ML are older than you might think. This is a rough time line with a few selected achievements from this field:\n",
     "\n",
     " \n",
     "    1812: Bayes Theorem\n",
     "    1913: Markov Chains\n",
     "    1951: First neural network\n",
+    "    1959: first use or term \"machine learning\" AI pioneer Arthur Samuel\n",
     "    1969: Book \"Perceptrons\": Limitations of Neural Networks\n",
     "    1986: Backpropagation to learn neural networks\n",
     "    1995: Randomized Forests and Support Vector Machines\n",
-    "    1998: Application of naive Bayes Classifier for Spam detection\n",
-    "    2000+: Deep learning"
+    "    1998: Public appearance of ML: naive Bayes Classifier for Spam detection\n",
+    "    2000+: Deep learning\n",
+    "    \n",
+    "So the field is not as new as one might think, but due to \n",
+    "\n",
+    "- more available data\n",
+    "- more processing power \n",
+    "- development of better algorithms \n",
+    "\n",
+    "more applications of machine learning appeared during the last 15 years."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## What are features ?\n",
+    "## Machine learning with Python\n",
+    "\n",
+    "Currently (2018) `Python` is the  dominant programming language for ML. Especially the advent of deep-learning pushed this forward. First releases of frameworks such as `TensorFlow` or `PyTorch` were released with`Python` support early.\n",
     "\n",
-    "In most cases we can arange data used for machine learning as a matrix:"
+    "The prevalent packages in the Python eco-system used for ML include:\n",
+    "\n",
+    "- `pandas` for handling tabualar data\n",
+    "- `matplotlib` and `seaborn` for plotting\n",
+    "- `scikit-learn` for classical (non-deep-learning) ML\n",
+    "- `tensorflow`, `PyTorch` and `Keras` for deep-learning.\n",
+    "\n",
+    "`scikit-learn` is very comprehensive and the online-documentation itself provides a good introducion into ML."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ML terms: What are \"features\" ?\n",
+    "\n",
+    "A typical and very common situation is that our data is presented as a table, as in the following example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -185,7 +203,7 @@
        "4         4.148710    0.570586  1.461568    0.260218         0"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -202,17 +220,18 @@
    "metadata": {},
    "source": [
     "\n",
-    "\n",
+    "**Definitions**:\n",
     "- every row of such a matrix is called a **sample** or **feature vector**. \n",
-    "- every column name is called a **feature name** or **attribute**.\n",
-    "- the cells are **feature values**."
+    "\n",
+    "- the cells in a row are **feature values**.\n",
+    "- every column name is called a **feature name** or **attribute**."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This table holds five samples.\n",
+    "This table shown holds five samples.\n",
     "\n",
     "The feature names are `alcohol_content`, `bitterness`, `darkness`, `fruitiness` and `is_yummy`."
    ]
@@ -223,7 +242,7 @@
    "source": [
     "(Almost) all machine learning algorithms require that your data is numerical and/or categorial. In some applications it is not obvious how to transform data to a numerical presentation.\n",
     "\n",
-    "Definition:\n",
+    "**Definition**:\n",
     "\n",
     "*Categorical data*: data which has only a limited set of allowed values. A `taste` feature could only allow values `sour`, `bitter`, `sweet`, `salty`."
    ]
@@ -232,16 +251,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### How to represent images as  feature vectors\n",
+    "A straight-forward application for machine-learning on the previos beer dataset is: **\"can we predict `is_yummy` from the other features\"** ?\n",
     "\n",
-    "Computers represent images as matrices. Every cell in the matrix represents one pixel, and the value in the matrix cell its color.\n",
+    "In this case we would call the features `alcohol_content`, `bitterness`, `darkness`, `fruitiness` our **input features** and `is_yummy` our **target value**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How to represent images as  feature vectors ?\n",
+    "\n",
+    "To simplify our explanations we consider gray images only here. Computers represent images as matrices. Every cell in the matrix represents one pixel, and the numerical value in the matrix cell its gray value.\n",
+    "\n",
+    "As we said, most machine learning algorithms require that every sample is represented as a  vector containing numbers. \n",
+    "\n",
+    "So how can we represent images as vectors then ?\n",
     "\n",
     "`scikit-learn`  includes some example data sets which we load now:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -286,9 +318,11 @@
    ],
    "source": [
     "N = 9\n",
+    "\n",
     "plt.figure(figsize=(2 * N, 5))\n",
-    "for i, image in enumerate(dd.images[:N], 1):\n",
-    "    plt.subplot(1, N, i)\n",
+    "\n",
+    "for i, image in enumerate(dd.images[:N]):\n",
+    "    plt.subplot(1, N, i + 1)\n",
     "    plt.imshow(image, cmap=\"gray\")"
    ]
   },
@@ -296,12 +330,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And this is the first image from the data set, it is a 8 x 8 matrix with values 0 to 15:"
+    "And this is the first image from the data set, it is a 8 x 8 matrix with values 0 to 15. The range 0 to 15 is fixed for this specific data set. Other formats allow e.g. values 0..255 or floating point values in the range 0 to 1."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -329,12 +363,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To transform such an image to a feature vectore we just have to concatenate the rows to one single vector of size 64:"
+    "To transform such an image to a feature vector we just have to concatenate the rows to one single vector of size 64:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -366,7 +400,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To transform some text into a feature vector, we first need a enumerated dictionary. Such a dictionary can be very large, but for the sake of simplicity we use a very small dictionary to explain the overall procedure:\n",
+    "If we start a machine learning project for texts, we first have to choose and fix an enumerated dictionary or words for this project. The final representation of texts as feature vectors depends on this dictionary. \n",
+    "\n",
+    "Such a dictionary can be very large, but for the sake of simplicity we use a very small enumerated dictionary to explain the overall procedure:\n",
     "\n",
     "\n",
     "| Word     | Index |\n",
@@ -384,7 +420,7 @@
     "\n",
     "| Word     | Index | Count |\n",
     "|----------|-------|-------|\n",
-    "| like     | 0     | 1     |\n",
+    "| like     | 0     | 0     |\n",
     "| dislike  | 1     | 1     |\n",
     "| american | 2     | 2     |\n",
     "| italian  | 3     | 0     |\n",
@@ -393,7 +429,9 @@
     "\n",
     "The according feature vector is the `Count` column, which is:\n",
     "\n",
-    "`[0, 1, 2, 0, 1, 1]`"
+    "`[0, 1, 2, 0, 1, 1]`\n",
+    "\n",
+    "In real case scenarios the dictionary is much bigger, this results then in vectors with only few non-zero entries (so called sparse vectors)."
    ]
   },
   {
@@ -405,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -420,13 +458,12 @@
     "from sklearn.feature_extraction.text import CountVectorizer\n",
     "from itertools import count\n",
     "\n",
-    "# map words to index in created vector:\n",
-    "vocabulary = [\"like\", \"dislike\", \"american\", \"italian\", \"beer\", \"pizza\"]\n",
+    "vocabulary = {\"like\": 0, \"dislike\": 1, \"american\": 2, \"italian\": 3, \"beer\": 4, \"pizza\": 5}\n",
     "\n",
-    "vectorizer = CountVectorizer(vocabulary=dict(zip(vocabulary, count())))\n",
+    "vectorizer = CountVectorizer(vocabulary=vocabulary)\n",
     "\n",
-    "# crate count vector for a pice of text:\n",
-    "vector = vectorizer.fit_transform([\"I dislike american pizza. But american beer is nice\"]).toarray()[0]\n",
+    "# create count vector for a pice of text:\n",
+    "vector = vectorizer.fit_transform([\"I dislike american pizza. But american beer is nice\"]).toarray().flatten()\n",
     "print(vector)"
    ]
   },
@@ -436,15 +473,17 @@
    "source": [
     "## Taxonomy of machine learning\n",
     "\n",
-    "We can separate learning problems in a few large categories: **supervised** and **unsupervised** learning.\n",
+    "Most applications of ML belong to two categories: **supervised** and **unsupervised** learning.\n",
     "\n",
-    "In **supervised learning** the the data comes with additional attributes that we want to predict. Such a problem can be either \n",
+    "### Supervised learning \n",
     "\n",
-    "- **classification**: samples belong to two or more discrete classes and we want to learn from already labeled data how to predict the class of unlabeled data. This is the same as saying, that the output is categorical.\n",
-    "    \n",
-    "- **regression**: if the desired output consists of one or more continuous variables, then the task is called regression.\n",
+    "In supervised learning the the data comes with an additional target value that we want to predict. Such a problem can be either \n",
+    "\n",
+    "- **classification**: we want to predict a categorical value.\n",
     "    \n",
+    "- **regression**: we want to predict numbers in a given range.\n",
     "    \n",
+    "  \n",
     "\n",
     "Examples for supervised learning:\n",
     "\n",
@@ -452,14 +491,38 @@
     "\n",
     "- Classification: predict the digit-shown based on a 8 x 8 pixel image (this is a multi-class problem).\n",
     "\n",
-    "- Regression: Predict the length of a salmon based on its age and weight.\n",
+    "- Regression: Predict the length of a salmon based on its age and weight."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unsupervised learning \n",
+    "\n",
+    "In unsupervised learning, in which the training data consists of samples without any corresponding target values, one tries to find structure in data. Some common applications are\n",
+    "\n",
+    "- Clustering: find groups in data.\n",
+    "- Density estimation: find a probability distribution in your data.\n",
+    "- Dimension reduction (e.g. PCA): find latent structures in your data.\n",
+    "\n",
+    "Examples for unsupervised learning:\n",
     "\n",
+    "- Can we split up our beer data set into sub groups of similar beers ?\n",
+    "- Can we reduce our data set because groups of features are somehow correlated ?\n",
+    "\n",
+    "<table>\n",
+    "    <tr>\n",
+    "    <td><img src=\"./cluster-image.png/\" width=60%></td>\n",
+    "    <td><img src=\"./nonlin-pca.png/\" width=60%></td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td><center>Clustering</center></td>\n",
+    "        <td><center>Dimension reduction: detecting 2D structure in 3D data</center></td>\n",
+    "    </tr>\n",
+    "</table>\n",
     "\n",
-    "In **unsupervised learning**, in which the training data consists of samples without any corresponding target values, one tries to find structure in data. Common applications are\n",
     "\n",
-    "- Clustering \n",
-    "- Density estimation\n",
-    "- Dimension reduction (PCA, ...)\n",
     "\n",
     "This course will only introduce concepts and methods from **supervised learning**."
    ]
@@ -472,18 +535,19 @@
     "\n",
     "Application of machine learning in practice consists of several phases:\n",
     "\n",
-    "1. Learn / train a model from example data\n",
+    "1. Understand and clean your data.\n",
+    "1. Learn / train a model \n",
     "2. Analyze model for its quality / performance\n",
     "2. Apply this model to new incoming data\n",
     "\n",
-    "In practice steps 1. and 2. are iterated for different machine learning algorithms until performance is optimal or sufficient. "
+    "In practice steps 1. and 2. are iterated for different machine learning algorithms with different configurations until performance is optimal or sufficient. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exercise section"
+    "# Exercise section 1"
    ]
   },
   {
@@ -495,7 +559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
@@ -580,25 +644,65 @@
        "4         4.148710    0.570586  1.461568    0.260218         0"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import pandas as pd\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.svm import SVC\n",
     "\n",
     "# read some data\n",
-    "\n",
     "beer_data = pd.read_csv(\"beers.csv\")\n",
-    "beer_data.head()"
+    "beer_data.head(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x11fb0c470>"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 774.8x720 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.set(style=\"ticks\")\n",
+    "\n",
+    "for_plot = beer_data.copy()\n",
+    "\n",
+    "for_plot[\"is_yummy\"] = for_plot[\"is_yummy\"].apply(lambda s: [\":-(\", \":-)\"][s])\n",
+    "sns.pairplot(for_plot, hue=\"is_yummy\", diag_kind=\"hist\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we split our data frame into the input features and target values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -622,40 +726,129 @@
     }
    ],
    "source": [
-    "# split matrix into features and labels\n",
-    "features = beer_data.iloc[:, :-1]\n",
+    "# all columns up to the last one:\n",
+    "input_features = beer_data.iloc[:, :-1]\n",
+    "\n",
+    "# only the last column:\n",
     "labels = beer_data.iloc[:, -1]\n",
     "\n",
-    "print(features.head())\n",
+    "print(input_features.head(5))\n",
     "print()\n",
-    "print(labels.head())"
+    "print(labels.head(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We experiment now the so called `LogisticRegression` classifier. The name is misleading: logistic regression internally uses a kind of regression algorithm for probabilities with the final goal to classify data. So even if the name contains \"regression\" it still is a classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier = LogisticRegression(C=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In `scikit-learn` all classifiers have a `fit` method to learn from data:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
+       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
+       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classifier.fit(input_features, labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Also `scikit-learn` classifiers have a `predict` method for predicting classes for input features. Here we just re-classify our learning data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicted_labels = classifier.predict(input_features)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets check our result with a few examples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LogisticRegression(C=2, class_weight=None, dual=False, fit_intercept=True,\n",
-      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
-      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
-      "          verbose=0, warm_start=False)\n"
+      "0 0\n",
+      "0 1\n",
+      "1 1\n",
+      "1 1\n",
+      "0 0\n"
      ]
     }
    ],
    "source": [
-    "classifier = LogisticRegression(C=1)\n",
-    "classifier.fit(features, labels)\n",
-    "print(classifier)"
+    "for i in range(5):\n",
+    "    print(labels[i], predicted_labels[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This looks suspicious !\n",
+    "\n",
+    "Lets investigate this further:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
@@ -663,14 +856,11 @@
      "output_type": "stream",
      "text": [
       "225 examples\n",
-      "199 labeled correctly\n"
+      "191 labeled correctly\n"
      ]
     }
    ],
    "source": [
-    "predicted_labels = classifier.predict(features)\n",
-    "\n",
-    "\n",
     "print(len(labels), \"examples\")\n",
     "print(sum(predicted_labels == labels), \"labeled correctly\")"
    ]
@@ -679,24 +869,60 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Comment\n",
-    "Are you surprised that not all labels where predicted correctly ?\n",
+    "Comment: `predicted_labels == labels` evaluates as a vector of values `True` or `False`. Python handles `True` as `1` and `False` as `0` when used as numbers. So the `sum(...)` just counts the correct results.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What happened ?\n",
+    "\n",
+    "Why were not not all labels where predicted correctly ?\n",
+    "\n",
+    "Neither `Python` nor `scikit-learn` is broken. What we observed above is very typical for machine-learning applications.\n",
+    "\n",
+    "The reason here is that we have incomplete information: other features of beer which also contribute to the rating (like \"maltiness\") where not measured or can not be measured. So even the best algorithm can not predict the target values reliably.\n",
+    "\n",
+    "Another reason might be mistakes in the input data, e.g. some labels are assigned incorrectly.\n",
+    "\n",
+    "* Finding good features is crucial for the performance of ML algorithms !\n",
     "\n",
-    "Reasons for this can be:\n",
-    "- missing information: maybe other features of beer which contribute to the rating where not measured or can not be measured.\n",
-    "- noisy information: features can be noisy"
+    "\n",
+    "* Another important issue is make sure that you have clean data: input-features might be corrupted by flawed entries, feeding such data into a ML algorithm will usually lead to reduced performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we play with a different ML algorithm, the so called `Support Vector Classifier` (which belongs to a class of algorithms named `SVM`s (`Support Vector Machines`):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "classifier = SVC(C=1)\n",
+    "classifier.fit(features, labels)\n",
+    "\n",
+    "predicted_labels = classifier.predict(features)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets evaluate the performance again:"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
@@ -710,19 +936,27 @@
     }
    ],
    "source": [
-    "classifier = SVC()\n",
-    "classifier.fit(features, labels)\n",
-    "\n",
-    "predicted_labels = classifier.predict(features)\n",
-    "\n",
     "print(predicted_labels.shape)\n",
     "print(labels.shape)\n",
     "print(sum(predicted_labels == labels))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a better result ! **But this does not indicate that `SVC` is always superior to `LogisticRegression`.**\n",
+    "\n",
+    "Here `SVC` just seems to fit better to our current machine learning task.\n",
+    "\n",
+    "### Instructions:\n",
+    "\n",
+    "- Play with parameter `C` for `LogisticRegresseion` and `SVC`.\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -753,17 +987,32 @@
        "        font-family: 'Lora', Consolas, sans-serif;\n",
        "       \n",
        "        -webkit-print-color-adjust: exact important !;\n",
+       "        \n",
+       "      \n",
        "       \n",
        "    }\n",
        "    .rendered_html code\n",
        "    {\n",
        "        color: black;\n",
        "        background: #eaf0ff;\n",
-       " \n",
+       "        background: #f5f5f5; \n",
        "        padding: 1pt;\n",
        "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
        "    }\n",
        "    \n",
+       "    p {\n",
+       "      line-height: 140%;\n",
+       "    }\n",
+       "    \n",
+       "    strong code {\n",
+       "        background: red;\n",
+       "    }\n",
+       "    \n",
+       "    .rendered_html strong code\n",
+       "    {\n",
+       "        background: #f5f5f5;\n",
+       "    }\n",
+       "    \n",
        "    .CodeMirror pre {\n",
        "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
        "    }\n",
@@ -773,8 +1022,13 @@
        "     }\n",
        "     \n",
        "     strong {\n",
-       "         background: #ffe7e7;\n",
-       "         padding: 1pt;\n",
+       "         background: #f5f5f5;\n",
+       "         margin-top: 4pt;\n",
+       "         margin-bottom: 4pt;\n",
+       "         padding: 2pt;\n",
+       "         border: 0.5px solid #a0a0a0;\n",
+       "         font-weight: bold;\n",
+       "         color: darkred;\n",
        "     }\n",
        "     \n",
        "    \n",
@@ -784,7 +1038,7 @@
        "        }\n",
        "        \n",
        "    li {\n",
-       "        line-heigt: 145%;\n",
+       "        line-height: 145%;\n",
        "    }\n",
        "\n",
        "    div.output_area pre {\n",
@@ -808,7 +1062,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -840,17 +1094,32 @@
     "        font-family: 'Lora', Consolas, sans-serif;\n",
     "       \n",
     "        -webkit-print-color-adjust: exact important !;\n",
+    "        \n",
+    "      \n",
     "       \n",
     "    }\n",
     "    .rendered_html code\n",
     "    {\n",
     "        color: black;\n",
     "        background: #eaf0ff;\n",
-    " \n",
+    "        background: #f5f5f5; \n",
     "        padding: 1pt;\n",
     "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
     "    }\n",
     "    \n",
+    "    p {\n",
+    "      line-height: 140%;\n",
+    "    }\n",
+    "    \n",
+    "    strong code {\n",
+    "        background: red;\n",
+    "    }\n",
+    "    \n",
+    "    .rendered_html strong code\n",
+    "    {\n",
+    "        background: #f5f5f5;\n",
+    "    }\n",
+    "    \n",
     "    .CodeMirror pre {\n",
     "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
     "    }\n",
@@ -860,8 +1129,13 @@
     "     }\n",
     "     \n",
     "     strong {\n",
-    "         background: #ffe7e7;\n",
-    "         padding: 1pt;\n",
+    "         background: #f5f5f5;\n",
+    "         margin-top: 4pt;\n",
+    "         margin-bottom: 4pt;\n",
+    "         padding: 2pt;\n",
+    "         border: 0.5px solid #a0a0a0;\n",
+    "         font-weight: bold;\n",
+    "         color: darkred;\n",
     "     }\n",
     "     \n",
     "    \n",
@@ -871,7 +1145,7 @@
     "        }\n",
     "        \n",
     "    li {\n",
-    "        line-heigt: 145%;\n",
+    "        line-height: 145%;\n",
     "    }\n",
     "\n",
     "    div.output_area pre {\n",
diff --git a/create_datasets.py.ipynb b/create_datasets.py.ipynb
index 9cee5d7d122cbf607607036d2446ac84fe93e00e..27d1661ca48295efd54d07581692db202ec7bb2e 100644
--- a/create_datasets.py.ipynb
+++ b/create_datasets.py.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -63,7 +63,7 @@
        "[4.274815584823238, 0.763271464942364, 1.6230700143217152, 0.3253729101618156]"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -177,7 +177,7 @@
        "max           5.955272    1.080170    7.221285    0.535315"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -208,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -287,7 +287,7 @@
        "4         4.148710    0.570586  1.461568    0.260218"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -298,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -364,18 +364,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol_content</th>\n",
+       "      <th>bitterness</th>\n",
+       "      <th>darkness</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.739295</td>\n",
+       "      <td>0.422503</td>\n",
+       "      <td>0.989463</td>\n",
+       "      <td>class_0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.207849</td>\n",
+       "      <td>0.841668</td>\n",
+       "      <td>0.928626</td>\n",
+       "      <td>class_0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.709494</td>\n",
+       "      <td>0.322037</td>\n",
+       "      <td>5.374682</td>\n",
+       "      <td>class_1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.684743</td>\n",
+       "      <td>0.434315</td>\n",
+       "      <td>4.072805</td>\n",
+       "      <td>class_1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.148710</td>\n",
+       "      <td>0.570586</td>\n",
+       "      <td>1.461568</td>\n",
+       "      <td>class_0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 796.725x720 with 20 Axes>"
+       "   alcohol_content  bitterness  darkness    label\n",
+       "0         3.739295    0.422503  0.989463  class_0\n",
+       "1         4.207849    0.841668  0.928626  class_0\n",
+       "2         4.709494    0.322037  5.374682  class_1\n",
+       "3         4.684743    0.434315  4.072805  class_1\n",
+       "4         4.148710    0.570586  1.461568  class_0"
       ]
      },
+     "execution_count": 13,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -385,12 +455,14 @@
     "for_plot = features.iloc[:, :-1].copy()\n",
     "for_plot[\"label\"] = [\"class_\" + li for li in labels.astype(str)]\n",
     "\n",
-    "sns.pairplot(for_plot, hue=\"label\", diag_kind=\"hist\");"
+    "for_plot.head()\n",
+    "\n",
+    "# sns.pairplot(for_plot, hue=\"label\", diag_kind=\"hist\");"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -402,7 +474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -412,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -497,7 +569,7 @@
        "4         4.148710    0.570586  1.461568    0.260218         0"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -509,7 +581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -588,7 +660,7 @@
        "4         4.148710    0.570586  1.461568    0.260218"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/requirements.txt b/requirements.txt
index 48f9c6fff9b7ce1afddc4346e123d0fdf5873874..cb2b1f2677f418fd6e6f8f27b8541bda249df1fb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,6 +5,7 @@ cycler==0.10.0
 decorator==4.3.0
 defusedxml==0.5.0
 entrypoints==0.2.3
+graphviz
 html5lib==1.0.1
 ipykernel==4.9.0
 ipympl==0.2.1