This page contains all public information about the course Machine Learning at the VU University Amsterdam. We provide the following materials:

Reuse is allowed under a creative commons license, details below.

All content

homework worksheets previous
w1

1. Introduction

playlist slides plain, answers getting set up, numpy 2020 2019 2018

2. Linear models and search

playlist slides 2020 2019 2018
w2

3. Model evaluation

playlist slides plain, answers sklearn 2020 2019 2018

4. Data pre-processing

playlist slides 2020 2019 2018
w3

5. Probabilistic Models

playlist slides plain, answers pandas 2020 2019 2018

6. Beyond Linear models

playlist slides 2020 2019 2018
w4

7. Deep Learning

playlist slides plain, answers keras 2020 2019 2018

8. Density estimation

playlist slides 2020 2019 2018
w5

9. Deep generative models

playlist slides plain, answers pytorch 2020 2019 2018

10. Tree Model and Ensembles

playlist slides 2020 2019 2018
w6

11. Models for Sequential Data

playlist slides plain answers 2020 2019 2018

12. Embedding models

playlist slides 2020 2019 2018
w7

13. Reinforcement Learning

playlist slides 2019 2018

14. Review

video slides 2019 2018
w8Exam. See below for practice exams.

Feel free to open a github issue if you’re working through the material and you spot a mistake, run into a problem or have any other kind of question. We also try to answer questions on youtube.

Required reading

Each week comes with a small amount of reading material. Most resources are publicly available free of charge. If you are a VU student, check Canvas for PDFs of the copyrighted works.

Week 1 Deep Learning, Goodfellow et al. Section 5.1
Week 2 Machine Learning, Peter Flach. Section 2.2
Everything you did and didn't know about PCA, Alex Williams
Week 3 Neural Networks and Deep Learning, Chapter 6
Week 4 What is the expectation maximization algorithm? Do et al.
Week 5 Intuitively Understanding Variational Autoencoders, Irhum Shafkat
Machine Learning, Tom Mitchell. Chapter 3.
Week 6 Understanding LSTM Networks, Chris Olah
Week 7 Reinforcement Learning: Pong from pixels, Andrej Karpathy

Practice exams

Each exam consists of 40 multiple choice questions.

Keynote files and re-use license

All material that is original to this course may be used under a CC BY 4.0 license. That means you are free to use the material, and adapt it, so long as appropriate credit is given. You may redistribute only under the same license.

How to credit:

If you would like to use the material, but do not want to attribute in this way for some reason, please get in touch. I’m sure we can work something out.

Some parts of the material are taken from other sources. The source should always be credited on the slide itself (let me know if this isn’t the case). Please adapt and redistribute these only under the original licenses.

Keynote files

The original keynote files for the lectures (of the 2019 version) can be found here, and may be used under the terms of the license above. These can be converted to ppt, but the formulas may not survive the conversion process.

The formulas were typeset using a fantastic tool called LaTeXiT. Copy pasting a formula from Keynote to LaTeXiT should reveal the original LaTeX. You’ll need to use this preample for the typesetting to work.