Winter Semester 2022/23, TU München

Organizers: Christian Tomani, Simon Schaefer Felix Wimbauer, Prof. Dr. Daniel Cremers

E-Mail: bdlstnc-ws22@vision.in.tum.de

The registration is managed via the TUM matching system website help. If you like this course, consider giving it a high priority in the matching system.

To apply for this seminar and get a priority, please also send us an email by July 27 to bdlstnc-ws22@vision.in.tum.de with the title “[Application] <Firstname> <Lastname>”, and attach your CV, transcript, and a filled course application form (rename to "firstname_lastname.xlsx"). Download the template for course application form here: Application template. (Please do not change the file format of the excel template!)

Deep learning models nowadays provide state of the art results and set a new standard for many applications, such as speech recognition, computer vision, predicting patients’ states in medicine as well as time series forecasting in finance.

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

• Time series models and post-calibration
• Bayesian deep learning models
• Graphical Models
• Alternative deep models and learning methods
• Metrics for evaluating uncertainty

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

The time and location of the pre-course meeting will be announced on the course website:

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
• Introduction to 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 (bdlstnc-ss22@vision.in.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 21st at 2pm-3pm, (Zoom, https://tum-conf.zoom.us/j/66551240061, Password: 209224)
• Kick-Off Meeting: October 19th at 11am-12noon, in person (02.09.023, MI Building)
• Final Presentations: January 17th and 18th from 9am-12noon, 1pm-4pm (Room TBD)

• 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.