Winter Semester 2024/25, TU Munich

Organizers Felix Wimbauer, Prof. Dr. Daniel Cremers

E-Mail: felix.wimbauer@tum.de (Use subject [BDL])

Preliminary meeting date: July 11th 2024, 10am

Preliminary meeting link: https://tum-conf.zoom-x.de/j/66862139226?pwd=3sOrmiSIlZBdMGQHpHn7N7DkTtLnBA.1

Deep learning models nowadays provide state of the art results and set a new standard for many applications, such as speech recognition, computer vision. As standard supervised deep learning has become more and more established, there has been great interest in more specialised topics.

This course will be focusing on advanced deep learning techniques. The topics will include:

• Uncertainty-aware Machine Learning
• Bayesian Deep Learning Models
• Diffusion Models
• Unsupervised Representation Learning (Transformer Pretraining, Object-Centric Learning)
• many more

We will be discussing state-of-the-art research and open issues in the scientific community.

Participants should already have a good understanding of basic machine learning and deep learning concepts and models. Especially, they are required to have taken at least one machine learning related course such as:

• Introduction to Deep Learning
• Machine Learning
• Machine Learning for Computer Vision
• Advanced Deep Learning for Computer Vision / Robotics
• Probabilistic Graphical Models in Computer Vision
• etc.

Participants should be able to take initiatives to plan and maintain a continuous workflow and communicate with tutors efficiently.

As many projects consider theoretical aspects of learning theory, a solid basis as well as interest for mathematics is highly recommended.

Prior experiences with machine learning projects are also a plus.

Note: it is crucial for interested applicants to also send us an e-mail (felix.wimbauer@tum.de) demonstrating their interest and fulfillment of prerequisites. The details will be explained during the pre-course meeting and available on the course website.

Places will be assigned through the TUM matching system (http://matching.in.tum.de).

This course is directed towards master students and will be held as a block seminar.

• Preliminary meeting: July 11th 2024, 10am-11am, online

• Deep Learning, Goodfellow, Bengio, Courville, 2016, http://www.deeplearningbook.org/
• Machine learning: a probabilistic perspective, Murphy, 2012
• The Elements of Statistical Learning, Hastie, Tibshirani, Friedman 2001
• Relevant papers will be announced during the course.