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Computer Vision Group
Faculty of Informatics
Technical University of Munich

Technical University of Munich

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Home Teaching Winter Semester 2017/18 Deep Learning for Computer Vision (IN2346) (2h + 2h, 6ECTS)

Deep Learning for Computer Vision (IN2346) (2h + 2h, 6ECTS)

WS 2017, TU München

Lecture

MOODLE
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Tuesdays (18:00-20:00) - 101, Interims Hörsaal 1 (5620.01.101)
Lecturers: Dr. Laura Leal-Taixé, Prof. Dr. Matthias Niessner
ECTS: 6
SWS: 4

Tutorial

Thursdays (18:00-20:00) - 00.02.001, MI HS 1, Friedrich L. Bauer Hörsaal (5602.EG.001)
Tutor: Thomas Frerix, Tim Meinhardt,Ji Hou,Andreas Rössler

Content
  • 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.
Prerequisites

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

EXERCISE 0:

  • Topics: Getting familiar with Python
  • Starting date: 17.10.2017
  • Due date: 01.11.2017
  • Recommended link: |Python introduction

EXERCISE 1:

  • Topics: Linear classifiers, multinomial regression, two-layer neural net.
  • Starting date: 02.11.2017
  • Due date: 15.11.2017

EXERCISE 2:

  • Topics: Fully connected nets, dropout, batch normalization.
  • Starting date: 16.11.2017
  • Due date: 29.11.2017

EXERCISE 3:

  • Topics: Convolutional neural networks, large-scale project with PyTorch.
  • Starting date: 30.11.2017
  • Due date: 13.12.2017

FINAL PROJECT:

  • 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

FINAL EXAM:

  • 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.

Contact us

If you have any questions regarding the organization of the course, do not hesitate to contact us at: dl4cv@vision.in.tum.de

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)

Rechte Seite

Informatik IX
Chair for Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching

info@vision.in.tum.de