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

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

SS 2017, TU München


Due to the high demand for the course we changed the schedule from Tuesday to Friday to get a bigger lecture room.

Thursday (16:00-18:00) - Walter-Hieber-Hörsaal (Chemistry building)
Friday (14:00-16:00) - MI Hörsaal 2
Lecturers: Dr. Laura Leal-Taixé, Prof. Dr. Matthias Niessner
SWS: 4


Date: on Fridays
Tutor: Thomas Frerix, Tim Meinhardt

  • Lecture 1 (27.04): Introduction to Computer Vision and history of Deep Learning.
  • Lecture 2 (28.04): Machine Learning basics 1: linear classification, maximum likelihood.
  • Lecture 3 (04.05): Machine Learning basics 2: logistic regression, perceptron 
  • Lecture 4 (11.05): Introduction to neural networks and their optimization, SGD, Back-propagation.
  • Lecture 5 (18.05): Training Neural Networks Part 1: regularization, activation functions, weight initialization, gradient flow, batch normalization, hyperparameter optimization.
  • Lecture 6 (01.06): Training Neural Networks Part 2: parameter updates, ensembles, dropout.
  • Lecture 7 (08.06): Convolutional Neural Networks.
  • Lecture 8 (22.06): CNN for object detection (from MNIST to ImageNet), visualizing CNN (DeepDream).
  • Lecture 9 (29.06): Prominent architectures: GoogleNet, ResNet.
  • Lecture 10 (06.07): Generative Adversarial nets + Recurrent networks (NLP).
  • Lecture 11 (13.07): LSTMs + Reinforcement Learning.
  • Special lecture (20.07,27.07): 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: Linear classifiers, multinomial regression, two-layer neural net.
  • Starting date: May 5th
  • Due date: May 17th


  • Topics: Fully connected nets, dropout, batch normalization.
  • Starting date: May 19th
  • Due date: May 31th


  • Topics: Convolutional neural networks, large-scale project with PyTorch.
  • Starting date: June 2nd
  • Due date: June 21st


  • Topic: each group will make a project proposal.
  • Project proposal due date: June 28th
  • Starting date: June 30th
  • Midterm handout is due: July 19th
  • Due date: August 10th
  • Poster presentation: August 17th
Lecture Slides


Contact us

If you have any questions regarding the organization of the course, do not hesitate to contact us at:

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 1-2pm (room 02.13.042)
Thomas Frerix: Thursdays 1-2pm (room 02.09.035)
Tim Meinhardt: Mondays 1-2pm (room 02.09.041)

Last edited 27.04.2017 12:58 by Thomas Frerix