Mathematical Foundations for Machine Learning

• Lecture period:	June 5th - July 14th
  Contact:	Thomas Schnake (t.schnake@tu-berlin.de)
  ISIS Course:	https://isis.tu-berlin.de/course/view.php?id=33631

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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).

More information can be found on xxx.

Structure

The weekly structure between the 5.6. and 7.7. is given below:

• Tue 10:15 - 11:45 am	Lecture
  Thu 10:15 - 11:45 am	Exercise

Exercise sheets will be collected in the lecture.

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
• Week 5 - Selected Subject - Mathematics in Machine Learning Today
• Week 6 - Online Test

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 but not necessary to attend the course. Students of all fields and universities are invited. The course will take place online, yet the final test will be held in person.

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.