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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich



Seminar: Beyond Deep Learning: Selected Topics on Novel Challenges (5 ECTS)

Summer Semester 2021, TU München

Organizers: Christian Tomani, Yuesong Shen, Prof. Dr. Daniel Cremers

E-Mail: bdlstnc-ss21@vision.in.tum.de


The Kick-Off meeting will take place on April 15th at 4:30pm. Course participants will be notified regarding the video conference.

The preliminary meeting took place on January 29th at 1-2pm via zoom. Slides are available here: slides

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 to bdlstnc-ss21@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.

Course Description

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-ss21@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).

Course Structure

This course will be held as a block seminar.

Course Schedule
  • Preliminary meeting: January 29th at 1-2pm, online, slides
  • Kick-Off Meeting: April 15th at 4:30pm, online
  • Final Presentations: June 29th from 1pm-4pm and June 30th 9am-4pm, 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.

Rechte Seite

Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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Rick Szeliski (University of Washington) will give a talk in the TUM AI lecture series on Jan 28th, 5pm! Livestream


Frank Dellaert (Georgia Tech) will give a talk in the TUM AI lecture series on Dec 17th, 4pm! Livestream


Jon Barron (Google) will give a talk in the TUM AI lecture series on Oct 22nd, 9pm! Livestream


We have five papers accepted to 3DV 2020!


Our effcient deep network architectures form the AI engine of the project Slow Down COVID-19 at Harvard.