Deep Learning
Fall Semester 2016
Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multilevel data representations.
In recent years, deep learning and deep networks have significantly improved the stateoftheart in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the fundamentals of deep learning and provide a rich set of handson tasks and practical projects to familiarize students with this emerging technology.
Course Catalogue InfoNews
11/08/2016  The webpage for this year's course is online. 
27/09/2016  Added slides lecture 1 + exercise 1. 
03/10/2016  Added slides lecture 2 + exercise 2 
10/10/2016  Added slides lecture 3. 
10/10/2016  Added exercise 3 + solutions for exercise 2. 
17/10/2016  Added slides lecture 4. 
18/10/2016  Added exercise 4 + solutions for exercise 3. 
24/10/2016  Added slides lecture 5. 
25/10/2016  Added exercise 5 + solutions for exercise 4. 
01/11/2016  Added exercise 6 + solutions for exercise 5. 
08/11/2016  Updated slides lecture 6 + added exercises 7 & solutions for exercise 6. 
14/11/2016  Added slides lecture 7 + exercise 8. 
21/11/2016  Added slides lecture 8. 
22/11/2016  Added exercise 9. 
28/11/2016  Updated lecture 8. 
18/01/2017  Added sample questions for the exam. 
Course Overview
Week  Date  Topic  Slides  Exercises  Material 

1  26.09.2016  Feedforward networks: architecture  slides 1  exercise 1  
2  03.10.2016  Feedforward networks: learning  slides 2  exercise 2  solutions 2 
3  10.10.2016  Optimization for neural networks  slides 3  exercise 3  solutions 3 
4  17.10.2016  Regularization for neural networks  slides 4  exercise 4  solutions 4 
5  24.10.2016  Convolutional Neural Networks  slides 5  exercise 5  solutions 5 
6  31.10.2016  Recurrent and recursive neural networks  slides 6 
exercise 6 TwoLayerCNN.py train_and_test_MNIST.py 
solutions 6 code solution 
7  07.11.2016  Recurrent and recursive neural networks (part 2) 
exercise 7  solutions 7 code solution 

8  14.11.2016  Factor models, autoencoders, latent representations  slides 7  exercise 8  code solution 
9  21.11.2016  28.11.2016  Undirected Deep Models & Generative models 
slides 8  exercise 9 Template (.ipynb) Template (.py) 
solutions 9 
  05.12.2016  No lecture (project)  No exercise session  
  12.12.2016  No lecture (project)  No exercise session  
  18.01.2017    sample exam questions 
Contact
Lecturer  Prof. Thomas Hofmann 
Assistant  Aurelien Lucchi 
Assistant  Florian Schmidt 
Assistant  Yannic Kilcher 
Assistant  Gary Becigneul 
Assistant  Javier Montoya 
Literature
Deep Learning, An MIT Press book, Ian Goodfellow, Yoshua Bengio and Aaron Courville