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

lecture homework worksheets 2018
week 1 Introduction slides, video plain, answers getting set up, numpy video
Linear models 1 slides, video video
week 2 Methodology 1 slides, video plain, answers sklearn video
Methodology 2 slides, video video
week 3 Probabilistic Models 1 slides, video plain, answers pandas video
Linear Models 2 slides, video video Contains more in-depth explanation of SVMs.
week 4 Deep Learning 1 slides, video plain, answers keras video
Probabilistic Models 2 slides, video video
week 5 Deep Learning 2 slides, video plain, answers video
Tree Model and Ensembles slides, video video
week 6 Models for Sequential Data slides, video plain, answers video
Matrix models slides, video video
week 7 Reinforcement Learning slides, video video
Review slides, video video
week 8Exam. 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.