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Mathematics for Machine Learning

Course: Winter Semester 2021

Prof. Dr. Gerard Pons-Moll

Continuous Learning of Multimodal Data Streams

University of Tuebingen



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ATTENDANCE REGISTRATION

Please fill out this form before attending the first lecture. Pre-registration is mandatory for lecture attendance. We only have 40 spots availabe. The portal will close once all the slots are taken.

Description

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. Tentatively, the following topics will be covered in the course:

Organization

This course is worth 9 ECTS points. Lectures will be delivered every week by Prof. Gerard Pons-Moll. Lectures will be delivered in person on Tuesday between 1200 and 1400. and on Wednesday between 0800 and 1000. Lectures will be recoreded and uploaded on moodle as well.
All lectures will be delivered in the main lecture hall of Maria von Linden Strasse 6, 72076 Tübingen. Due to covid, we will have to limit the number of participants to roughly 40. To attend you need to present proof of vaccination or recovery, or a negative corona test. The lecture will also be available via zoom. The zoom link will be made available in Moodle.
There will be two weekly tutorials as well - on Thursday between 1200 and 1400 in Lecture Hall A301 (Sand 1) and on Friday between 1000 and 1200 in Lecture Hall 1 F119 (Sand 6/7). To attend a tutorial, you need to present proof of vaccination or recovery, or a negative corona test. The TAs for the course are:

Assignments

There will be weekly assignments that you have to solve in groups of three students. Achieving half of the possible points is a formal requirement for being admitted to the exam. This is a purely theoretical course and all assignments will be theoretical in nature.

Exam

The final exams will take place on-site in Tuebingen, and you need to be physically present. There is going to be one exam at the beginning of the semester break and one at the end of the semester break. The general mode for exams is: You are not allowed to bring any material (books, slides, etc) except for what we call the controlled cheat sheet: one side (A4, one side only) of handwritten (!) notes, made by yourself. This cheat sheet has to be handed in together with the exam.

Course Content

Lecture slides and assignment sheets will be distributed via moodle

Schedule

Date No. Title Exercise
Tu. 19.10. L1 Introduction
We. 20.10. L2 Linear Algebra
Tu. 26.10. L3 Linear Algebra
We. 27.10. L4 Linear Algebra Ex01 Out
Tu. 02.11. L5 Linear Algebra Ex01 Due
We. 03.11. L6 Linear Algebra Ex02 Out
Tu. 09.11. L7 Linear Algebra Ex02 Due
We. 10.11. L8 Linear Algebra Ex03 Out
Tu. 16.11. L9 Linear Algebra Ex03 Due
We. 17.11. L10 Calculus Ex04 Out
Tu. 23.11. L11 Calculus Ex04 Due
We. 24.11. L12 Calculus Ex05 Out
Tu. 30.11. L13 Optimization Ex05 Due
We. 01.12. L14 Optimization Ex06 Out
Tu. 07.12. L15 Optimization Ex06 Due
We. 08.12. L16 Optimization Ex07 Out
Tu. 14.12. L17 Optimization Ex07 Due
We. 15.12. L18 Optimization Ex08 Out
Tu. 21.12. L19 Optimization Ex08 Due
We. 22.12. L20 Optimization Ex09 Out
Tu. 11.01. L21 Probability Ex09 Due
We. 18.01. L22 Probability Ex10 Out
Tu. 19.01. L23 Probability Ex10 Due
We. 15.1. L24 Probability Ex11 Out
Tu. 25.01. L25 Statistics Ex11 Due
We. 26.01. L26 High Dimensions Ex12 Out
Tu. 01.02. L25 TBD Ex12 Due
We. 02.02. L26 TBD

Literature:

To go a bit deeper into the topics we cover in this course, the following textbooks are ideal.