Machine Learning 2 / Machine Learning 2-X

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):

Do I need to pre-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'': (1) Print out the forms concerning Nebenhörerschaft you find on that page. (2) Email the course contact (cf. above) to have them signed. (3) In addition, the dean of faculty IV has to sign. (4) Register at the the Campus Center. You will receive a TUBIT account.

Topics

• Embeddings (LLE, TSNE)
• Component Analyses (CCA, ICA)
• Kernel Learning (structured input, structured outputs, bioinformatics, anomaly detection)
• Deep Learning (convolutional neural networks, generative adversarial networks, XAI)
• Federated Learning

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

• Lecture Series on Machine Learning in Applications

Note that these courses cannot be taken as standalone courses.