Mathematics for Machine Learning (Winter term 2024/25)
THIS PAGE IS STILL UNDER CONSTRUCTION.Quick links
 Ilias for registration (link will come in September)
 Alma course announcement (link still to come)

Youtube channel (lectures of 2020)
Background information
This course is intended for master students who plan to dive further in machine learning. Depending on your background, much of the material might be a recap  or not. Contents of the course are Linear algebra, Mulitvariate analysis, Probability Theory, Statistics, Optimization.
Registration
You need to register for this course in Ilias, link to come in september. Registration can happen until the first week of term, and everybody will be admitted, so don't worry if the link is not yet available.Setup
What: Mathematics for Machine Learning, 9 CPLecturer: Prof. Ulrike von Luxburg
Assignments and tutorials are organized by: Eric Guenther
Lectures, when and where: tba Tutorials, when and where: several groups, you can give choices during registration.
Tutorials
We will have weekly tutorial sessions in small groups of about 2030 students, where you can ask questions and interact with other students. You will be able to enter your preferences regarding the time when you register for the tutorials. The teaching assitants are: tbaLecture notes
My Lecture notes of 2020:
Linear algebra (A): pdf

Calculus (C): pdf
 Probability theory (P):
pdf
 Statistics (S):
pdf
 Mixed materials (H):
pdf
 Lecture notes by Armando Cabrera, who taught the course last year.
 Lecture notes by Matthias Hein, who taught the course before Armando.
Literature:
General: Deisenroth, Faisal, Ong: Mathematics for Machine Learning, 2019. Not as deep as what we do in this class, but a good start.
 For linear algebra, I recommend: Sheldon Axler: Linear Algebra Done Right. Third edition, 2015. There are also online videos by the author if you want to get longer explanations than the ones I will provide.
 Calculus (Integration, Measures, Metric spaces and their topology): Sheldon Axler: Measure, Integration & Real Analysis. 2019
 Calculus (Differential calculus in R^n): Here I haven't found
my favorite english textbook yet. Below are some references, but the
first one is slightly to recipelike, the other too abstract. Still
watching out for a good compromise...
 Books with many figures, but partly informal or recipelike. Might be good as a start if you need to get the intuition before diving deeper:
Stanley Miklavcic: An Illustrative Guide to Multivariable and Vector Calculus.
Charles Pugh: Real Mathematical Analysis  Mathematically rigorous, but not easy to read:
Terence Tao, Analysis 1 and 2. (just discovered it, love it!).
Rudin: Principles of Mathematical Analysis. (A classic, sometimes called the BabyRudin).  Calculus, a german book I like: Walter: Analysis 1 and Analysis 2. The second one covers everything that we have been discussing.
 Books with many figures, but partly informal or recipelike. Might be good as a start if you need to get the intuition before diving deeper:
 Probability theory: Jacod, Protter: Probability essentials. Short and to the point, tries to avoid measure theory whereever possible, yet is rigorous. Good compromise.
 Statistics:
 For a very short overview over all the topics we cover: Wasserman: All of statistics, a concise course in statisticial inferece.
 A bit more details: Casella/Berger, Statistical Inference.
 Testing, rigorously: Lehmann/Romano: Testing statistical hypotheses.
 For highdimensional probability and statistics there are several good books, but they go
much deeper than our lecture:
 Wainwritght: Highdimensional statistics
 Vershynin: Highdimensional probability
 Bühlmann, van de Geer: Statistics for Highdimensional data (this is from the more traditional statitics point of view)