Machine Learning 2

General Information

• Machine Learning 2 is a 9 LP (9 ECTS) credits module.
• Machine Learning 2-X is a 12 LP (12 ECTS) credits module.

Frequently asked questions (FAQ):

• How to register for the course? There is no pre-registration. Just come to the first lecture.

• Is it possible to take this course without having taken ML1? Yes, ML1 is not a formal prerequisite. However, methods learned in ML1 will be assumed to be known, and extra work might therefore be needed during the first few weeks.

• I am from a different university, can I take this course? If you are not a student at TU and want to earn credit, you have to solicit ''Nebenhörerschaft''.

Topics

• Embeddings (LLE, TSNE)
• Component Analyses (CCA, ICA)
• Kernel Learning (structured input, structured outputs, applications to bioinformatics and anomaly detection)
• Hidden Markov Models
• Deep Learning (convolutional neural networks, generative adversarial networks, Explainable AI)

List of elective courses

As part of Machine Learning 2-X, you need to take one of the following courses:

• Python Programming for Machine Learning

• Seminar Hot Topics in Machine Learning

• Joint Seminar on Machine Learning and Data Management Systems

• Joint Seminar on Machine Learning for Quantum Chemistry

• Machine Learning in Science and Industry

Note that these courses cannot be taken as standalone courses.