Direkt zum Inhalt springen
Computer Vision Group
Faculty of Informatics
Technical University of Munich

Technical University of Munich

Menu
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

Lecture

MOODLE
We use Moodle for discussions and to distribute important information. Please check the News and Discussion boards regularly or subscribe to them.

NEW LOCATION AND SCHEDULE!
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
ECTS: 6
SWS: 4

Tutorial

Date: on Fridays
Tutor: Thomas Frerix, Tim Meinhardt

Content
  • 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.
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 1:

  • Topics: Linear classifiers, multinomial regression, two-layer neural net.
  • Starting date: May 5th
  • Due date: May 17th

EXERCISE 2:

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

EXERCISE 3:

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

FINAL PROJECT:

  • Topic: each group will make a project proposal.
  • Project proposal due date: June 28th
  • Starting date: June 30th
  • UPDATED!! Poster due date: Monday, August 7th, 23:59 (details in moodle news post)
  • UPDATED!! Poster presentation: Thursday, August 10th

FINAL EXAM:

  • Exam date: August 18th

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.

Lecture Slides

tba.

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