Mathematical Foundations for Machine Learning
Lecture period:
30.05. - 01.07.
Contact:
Thomas Schnake (t.schnake@tu-berlin.de)
ISIS Course:
Information
The goal of this course is to freshen and deepen the mathematical foundations from the computer science program that are necessary for the lectures Cognitive Algorithms and Machine Learning.
Topics of the course come from analysis (differentiation), linear algebra (vector spaces, dot products, orthogonal vectors, matrices as linear maps, determinants, eigenvalues and eigenvectors) and probability theory (multivariate probability distributions, calculations with expectation values and variances).
Structure
Attendance is not mandatory. The structure is roughly given below:
10:00 – 11:30 am
Introductory lecture
11:30 – 3:00 pm
Work on exercise sheets
3:00 – 4:00 pm
Review of exercise sheets
4:00 – 5:00 pm
Work on homework sheets
Preliminary structure:
- Week 1 - Linear Algebra I: Groups, Fields and Euclidean Vector Spaces
- Week 2 - Linear algebra II: Linear Transformations, Matrices and Determinants
- Week 3 - Analysis: Differentiation and ML Examples
- Week 4 - Probability Theory
Credits
The course is part of the module Machine Learning 1-X (M.Sc. Informatik) and optional for Cognitive Algorithms (B.Sc. Informatik).
Registration is desired until 15.05. but necessary to attend the course, students of all fields and universities are invited. The decision wether the course will take place in person or online, depends on the number of attendees, and will be announced about around the 23.05./* Basis for passing the course is a test (90 minutes). Prerequisite for the participation in the test is the achievement of at least half of all possible points in the homework, the results in the exercises are not included in the grade. */