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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

Lecture

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
  • Midterm handout is due: July 19th
  • Due date: August 10th
  • Poster presentation: August 17th
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.

Last edited 20.04.2017 17:57 by Laura Leal-Taixe