This page contains all public information about the course Machine Learning at the VU University Amsterdam. We provide the following materials:
- Lecture slides and videos.
- Worksheets These are very brief Jupyter notebooks to help you get the software installed and to show the basics. They introduce the libraries Numpy, Matplotlib, Pandas, Sklearn and Keras.
- Homework The homework consists of small pen-and-paper exercises to help you test that you’ve really understood the more technical points of the lectures. Answers are provided. If you are a registered student, please refer to the Canvas page instead.
|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|
|week 8||Exam. 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.
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|
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|
Each exam consists of 40 multiple choice questions.