Deep Learning for Computer Vision (IN2346) (2h + 2h, 6ECTS)
WS 2017, TU München
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- 17.10 - Lecture 1: Introduction to Computer Vision and history of Deep Learning.
- 19.10 - Lecture 2: Machine Learning basics: linear regression, maximum likelihood.
- 24.10 - Lecture 3: Introduction to neural networks, Back-propagation.
- 07.11 - Lecture 4: Optimization
- 21.11 - Lecture 5: Training Neural Networks Part 1: regularization, activation functions, weight initialization, gradient flow, batch normalization, hyperparameter optimization.
- 28.11 - Lecture 6: Training Neural Networks Part 2: parameter updates, ensembles, dropout.
- 05.12 - Lecture 7: Convolutional Neural Networks.
- 12.12 - Lecture 8: CNN 2
- 19.12 - Lecture 9: CNN 3
- 09.01 - Lecture 10: CNN 4
- 16.01 - Lecture 11: Generative networks, GANs, VAE
- 23.01 - Lecture 12: Recurrent networks and LSTMs
- 30.01 - Lecture 13: Reinforcement Learning
- 06.02 - Lecture 14: Special lecture to be announced.
Passion for mathematics and the use of machine learning in order to solve complex computer vision problems. The course will be focused on practical projects, therefore, previous knowledge of a programming language, preferably Python , is desired.
Tentative exercise schedule
- Topics: Getting familiar with Python
- Starting date: 17.10.2017
- Due date: 01.11.2017
- Recommended link: |Python introduction
- Topics: Linear classifiers, multinomial regression, two-layer neural net.
- Starting date: 02.11.2017
- Due date: 15.11.2017
- Topics: Fully connected nets, dropout, batch normalization.
- Starting date: 16.11.2017
- Due date: 29.11.2017
- Topics: Convolutional neural networks, large-scale project with PyTorch.
- Starting date: 30.11.2017
- Due date: 13.12.2017
- Topic: each group will make a project proposal.
- Project proposal due date: 19.12.2017
- Starting date: 21.12.2017
- Poster due date: tba
- Poster presentation: tba
- Exam date: tba
For details about the exercises, please read the README, which is distributed with each exercise. In particular, you will submit your solutions via this website.
If you have any questions regarding the organization of the course, do not hesitate to contact us at: firstname.lastname@example.org
For questions on the syllabus, exercises or any other questions on the content of the lecture, we will use the Moodle discussion board.
We offer to discuss questions in person in our office hours:
Dr. Laura Leal-Taixé: Wednesdays 1-2pm (room 02.09.044)
Prof. Dr. Matthias Niessner: Tuesdays 2-3pm (room 02.13.042)
Thomas Frerix: Thursdays 1-2pm (room 02.09.035)
Tim Meinhardt: Mondays 1-2pm (room 02.09.041)