fix with comments from r

README.md

@@ -1,25 +1,27 @@
 # Machine Learning Exercises
 
-This repository contains the Python exercises accompanying the theory from my [machine learning book](https://franziskahorn.de/mlbook/).
+This repository contains the exercises accompanying the theory from my [machine learning book](https://franziskahorn.de/mlbook/).
 
 You might also want to have a look at the [cheat sheet](/cheatsheet.pdf), which includes a summary of the most important steps when developing a machine learning solution, incl. code snippets.
 
-If you're unfamiliar with Python, please have a look at [this tutorial](https://github.com/cod3licious/python_tutorial) before working on the exercises, which also includes some notes on how to install Python and Jupyter Notebook on your own computer (please make sure you're using Python 3 and all libraries listed in the `requirements.txt` file are installed and up to date; you can verify this with the `test_installation.ipynb` notebook). <br>
+The programming exercises are written in Python. If you're unfamiliar with Python, please have a look at [this tutorial](https://github.com/cod3licious/python_tutorial) before working on the exercises, which also includes some notes on how to install Python and Jupyter Notebook on your own computer (please make sure you're using Python 3 and all libraries listed in the [`requirements.txt`](/requirements.txt) file are installed and up to date; you can verify this with the [`test_installation.ipynb`](/test_installation.ipynb) notebook). <br>
 If you have a Google account, you can also run the code in the cloud using **Google Colab**:
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cod3licious/ml_exercises)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cod3licious/ml_exercises) <br>
+While Google Colab already includes most packages that we need, should you require an additional library (e.g., `skorch` for training PyTorch neural networks in notebook 5), you can install it by executing `!pip install package` in a notebook cell. With Colab, it is also possible to run code on a GPU, but this has to be manually selected.
 
-If you have any questions, please drop me a line at `hey[at]franziskahorn.de`.
+If you have any questions, please send me an [email](mailto:hey@franziskahorn.de).
 
 Have fun!
 
 
 ## Course Overview
 
-(You can also find the course syllabus on the last page of the [course description](/course_description.pdf).)
+For an optimal learning experience, the chapters from the [machine learning book](https://franziskahorn.de/mlbook/) should be interleaved with quizzes and programming exercises as shown below.
+(You can also find the course syllabus on the last page of the [course description](/course_description.pdf), which explicitly lists all the sections of the book for each block.)
 
 ---
 
-### Part 1:
+### Part 1: Getting started: What is ML?
 
 ##### Block 1.1:
 - [ ] Read the whole chapter: ["Introduction: Solving Problems with ML"](https://franziskahorn.de/mlbook/_introduction_solving_problems_with_ml.html)
@@ -32,11 +34,11 @@ Have fun!
 ##### Block 1.3:
 - [ ] Read the whole chapter: ["Data & Preprocessing"](https://franziskahorn.de/mlbook/_data_preprocessing.html)
 - [ ] Answer [Quiz 2](https://forms.gle/Pqr6EKHNxzrWb7MF9)
-- [ ] Read the first section of the chapter ["ML Algorithms: Unsupervised & Supervised Learning"](https://franziskahorn.de/mlbook/_ml_algorithms_unsupervised_supervised_learning.html)
+- [ ] Read the introductory part of the chapter ["ML Algorithms: Unsupervised & Supervised Learning"](https://franziskahorn.de/mlbook/_ml_algorithms_unsupervised_supervised_learning.html)
 
 ---
 
-### Part 2:
+### Part 2: Your first algorithms
 
 ##### Block 2.1:
 - [ ] Read the section: ["UL: Dimensionality Reduction"](https://franziskahorn.de/mlbook/_ul_dimensionality_reduction.html)
@@ -53,11 +55,11 @@ Have fun!
 
 ---
 
-### Part 3:
+### Part 3: Advanced models
 
 ##### Block 3.1:
 - [ ] Read the sections: ["SL: Linear Models"](https://franziskahorn.de/mlbook/_sl_linear_models.html) up to and including ["SL: Kernel Methods"](https://franziskahorn.de/mlbook/_sl_kernel_methods.html)
-- [ ] In parallel, work through the respective sections of [Notebook 3: supervised comparison](/exercises/3_supervised_comparison.ipynb)
+- [ ] **In parallel**, work through the respective sections of [Notebook 3: supervised comparison](/exercises/3_supervised_comparison.ipynb)
 
 ##### Block 3.2:
 - [ ] Read the section: ["Information Retrieval (Similarity Search)"](https://franziskahorn.de/mlbook/_information_retrieval_similarity_search.html) and review the sections on [TF-IDF feature vectors](https://franziskahorn.de/mlbook/_feature_extraction.html) and [cosine similarity](https://franziskahorn.de/mlbook/_computing_similarities.html)
@@ -70,7 +72,7 @@ Have fun!
 
 ---
 
-### Part 4:
+### Part 4: Avoiding common pitfalls
 
 ##### Block 4.1:
 - [ ] Read the whole chapter: ["Avoiding Common Pitfalls"](https://franziskahorn.de/mlbook/_avoiding_common_pitfalls.html)
@@ -84,7 +86,7 @@ Have fun!
 
 ---
 
-### Part 5:
+### Part 5: RL & Conclusion
 
 ##### Block 5.1:
 - [ ] Read the whole chapter: ["ML Algorithms: Reinforcement Learning"](https://franziskahorn.de/mlbook/_ml_algorithms_reinforcement_learning.html)