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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


Location: Room 02.09.023
Date: Tuesday (14:00-16:00) - Thursday (16:00-18:00)
Lecturer: Dr. Laura Leal-Taixé
SWS: 4


Location: Room 02.09.023
Date: one hour each lecture day
Tutor: Thomas Frerix, Caner Hazirbas

  • Introduction to Computer Vision and history of Deep Learning.
  • Machine learning Basics 1: linear classification, maximum likelihood
  • Machine learning basics 2: logistic regression, perceptron 
  • Introduction to neural networks and their optimization, SGD, Back-propagation
  • Training Neural Networks Part 1: regularization, activation functions, weight initialization, gradient flow, batch normalization, hyperparameter optimization
  • Training Neural Networks Part 2: parameter updates, ensembles, dropout
  • Convolutional Neural Networks
  • CNN for object detection (from MNIST to ImageNet), visualizing CNN (DeepDream)
  • Recurrent networks and LSTMs
  • Research 1: Prominent architectures, e.g. GoogleNet, ResNet
  • Research 2: Reinforcement learning
  • Research 3: Adversarial networks

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 a programming language, preferably Python, is desired.

Lecture Slides


Last edited 21.02.2017 13:06 by Caner Hazirbas