Beyond Deep Learning: Uncertainty Aware Models (IN2106, IN4278)
Course 0000004443 in SS 2020
General Data
| Course Type | practical training |
|---|---|
| Semester Weekly Hours | 6 SWS |
| Organisational Unit | Informatics 9 - Chair of Computer Vision and Artificial Intelligence (Prof. Cremers) |
| Lecturers |
Björn Häfner Responsible/Coordination: Daniel Cremers Assistants: Yuesong Shen Christian Tomani |
| Dates |
4 singular or moved dates |
Assignment to Modules
-
IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:- Further Modules from Other Disciplines
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | 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. However, these models generally lack the ability to correctly estimate uncertainty, which is crucial for real world applications. This course will be focusing on developing deep learning models with a particular focus on uncertainty awareness. 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 - Real world datasets We will be tackling real world problems and will be working on open issues in the scientific community. Note: it is crucial for interested applicants to also send us an e-mail (bdluam-ss20@vision.in.tum.de) demonstrating their interest & fulfillment of prerequisites. The details will be explained during the pre-course meeting & available on the website. The time and location of the pre-course meeting will be announced on the course website: https://vision.in.tum.de/teaching/ss2020/bdluam_ss2020 |
|---|---|
| Links | TUMonline entry |