Mathematics of Machine Learning 2022

This is the main website for the Mathematics of Machine Learning course in the spring of 2022, as part of the bachelor of mathematics at the University of Amsterdam. Visit this page regularly for changes and updates.

Instructor: Tim van Erven (tim@ No spam, please timvanerven. No really, no spam nl)
Teaching Assistants: Jack Mayo (j.j.mayo@ No spam, please uva. No really, no spam nl)
  Boris Lebedenko (b.lebedenko@ No spam, please uva. No really, no spam nl)

General Information

Machine learning is one of the fastest growing areas of science, with far-reaching applications. This course gives an overview of the main techniques and algorithms. The lectures introduce the definitions and main characteristics of machine learning algorithms from a coherent mathematical perspective. In the exercise classes the algorithms will be implemented in Python and applied to a selection of data sets.

Required Prior Knowledge

  • Linear algebra, gradients, convexity
  • Programming in Python
  • Writing in LaTeX

Although mainly targeting mathematics students, the course should be accessible to other science students (AI, CS, physics, …) with an interest in mathematical foundations of machine learning.

Lectures and Exercise Sessions

Lecture hours TBA

Examination Form

The course grade consists of the following components:

  • Homework assignments. H = Average of homework grades.
  • Two exams: midterm and final. E = Average of the two exam grades.

The final grade is computed as \(\max\big\{\mathrm{\textbf{E}}, \frac{1}{3}\mathrm{\textbf{H}} + \frac{2}{3}\mathrm{\textbf{E}}\big\}\), rounded.

Course Materials

The main book for the course is The Elements of Statistical Learning (ESL), 2nd edition, by Hastie, Tibshirani and Friedman, Springer-Verlag 2009. In addition, we will use selected parts from Ch.18 of Computer Age Statistical Inference: Algorithms, Evidence and Data Science (CASI) by Efron and Hastie, Cambridge University Press, 2016. Some supplementary material will also be provided, as listed in the Course Schedule.

Both books are freely available online, but it is recommended to buy a paper copy of the ESL book, because you will need to study many of its chapters. The standard edition of ESL is hard cover, but there also exists a much cheaper soft-cover edition for €24.99. To get the cheaper offer, open this link from inside the university network.

About using Wikipedia and other online sources: trust Wikipedia as much as you would trust a fellow student who is also still learning. Some things are good, but other things are poorly explained or plain wrong, so always verify with a trusted source (a book or scientific paper). This holds doubly for any ‘data science’ blogs you might find online.

Course Schedule


Homework Assignments

The homework assignments will be made available here.