Extra resources

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. All material authored by Peter Bloem unless noted otherwise.

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

All content

			homework	worksheets	previous
w1	1. Introduction 1.1 What is machine learning? 1.2 Classification 1.3 Other abstract tasks 1.4 Social impact 1 1.5 Generalization	playlist slides	plain, answers	getting set up, numpy	2020 2019 2018
	2. Linear models and search 2.1 Linear regression 2.2 Searching for a good model 2.3 Gradient descent 2.4 Linear classification	playlist slides			2020 2019 2018
w2	3. Model evaluation 3.1 Machine learning experiments 3.2 Model evaluation 3.3 ROC curves, area under the curve 3.4 Social impact 2 3.5 Statistics for ML experiments 3.6 No free lunch	playlist slides	plain, answers	sklearn	2020 2019 2018
	4. Data pre-processing 4.1 Missing values and outliers 4.2 Class imbalance and feature design 4.3 Normalization 4.4 Principal component analysis	playlist slides			2020 2019 2018
w3	5. Probabilistic Models 5.1 Introduction to probability 5.2 Learning with probability 5.3 The (naive) Bayes classifier 5.4 Logistic regression 5.5 Information theory	playlist slides	plain, answers	pandas	2020 2019 2018
	6. Beyond Linear models 6.1 Neural networks 6.2 Backpropagation 6.3 Support vector machines 6.4 Lagrange multipliers 6.5 The kernel trick	playlist slides			2020 2019 2018
w4	7. Deep Learning 7.1 Deep learning and automatic differentiation 7.2 Tensor backpropagation 7.3 Convolutions 7.4 Making it work	playlist slides	plain, answers	keras	2020 2019 2018
	8. Density estimation 8.1 Normal distributions 8.2 Maximum likelihood estimators 8.3 Expectation-maximization 8.4 Expectation-maximization from first principles 8.5 Social impact 3	playlist slides			2020 2019 2018
w5	9. Deep generative models 9.1 Generator networks 9.2 Generative adversarial networks 9.3 Autoencoders 9.4 Variational autoencoders	playlist slides	plain, answers	pytorch	2020 2019 2018
	10. Tree Model and Ensembles 10.1 Decision trees 10.2 Regression trees and numeric features 10.3 Ensembling 10.4 Boosting	playlist slides			2020 2019 2018
w6	11. Models for Sequential Data 11.1 Markov models 11.2 Deep learning on sequences 11.3 Recurrent neural nets and LSTMs	playlist slides	plain answers		2020 2019 2018
	12. Embedding models 12.1 Recommender systems 12.2 Improving recommender systems 12.3 PCA revisited 12.4 Graph models 12.5 Validation of embedding models	playlist slides			2020 2019 2018
w7	13. Reinforcement Learning 13.1 Reinforcement learning 13.2 Policy gradients and random search 13.3 (Deep) Q earning 13.4 AlphaGo 13.5 social impact 4	playlist slides			2019 2018
	14. Review	video slides			2019 2018
w8	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.

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.

• Final exam 2018, answers
• Resit 2018, answers

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:

• For individual slides, please add a link to mlvu.github.io, on the slide, or in the published slide annotations.
• If you are using a slide deck for a lecture as is, please indicate the source of the slides as mlvu.github.io clearly at the start of the lecture. Leaving the existing URL in place on the opening slide suffices.
• If you use many of the slides, a single attribution can be made once at the start of the slide deck. Crediting me by name (Peter Bloem) is appreciated, but not strictly necessary.

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.