Statistical Machine Learning

This course provides an introduction to modern statistical machine learning, in particular probabilistic models.
Syllabus:
Introduction to Machine Learning
Generative Probabilistic Models
Discriminative Probabilistic Models
Structural Risk Minimization
Kernel Methods
Deep Learning
Statistical Learning Theory
Model Selection and Feature Selection
Undersupervised Learning

Target group: Students from all scientific disciplines with interest in machine learning as a research topic.

Prerequisites: mathematics: linear algebra, calculus, probabilities
programming skills in a language that allows numeric computation, such as Python

Evaluation: 50% homework, 50% final project

Teaching format: None

ECTS: 3 Year: 2020

Track segment(s):
CS-AI Computer Science - Artificial Intelligence
DSSC-PROB Data Science and Scientific Computing - Probabilistic Models

Teacher(s):
Christoph Lampert

Teaching assistant(s):
Elena-Alexandra Peste Bernd Prach

If you want to enroll to this course, please click: REGISTER