Spring 2018 / DM863
Deep Learning

General Information

Machine learning has become a part in our everydays life, from simple product recommendations to personal electronic assistant to self-driving cars. More recently, through the advent of potent hardware and cheap computational power, “Deep Learning” has become a popular and powerful tool for learning from complex, large-scale data.

In this course, we will discuss the fundamentals of deep learning and its application to various different fields. We will learn about the power but also the limitations of these deep neural networks. At the end of the course, the students will have significant familiarity with the subject and will be able to apply the learned techniques to a broad range of different fields.

Mainly, the following topics will be covered:

  • feedforward neural networks
  • recurrent neural networks
  • convolutional neural networks
  • backpropagation algorithm
  • regularization
  • factor analysis


# Date Content Slides Comments
1 Tue, 06.02.2018 Introduction here
2 Thu, 08.02.2018 Recap: Math here
3 Tue, 13.02.2018 Machine Learning Basics here
4 Thu, 15.02.2018 Feed Forward Networks: Part I here
5 Tue, 20.02.2018 Continuation of last Lecture --
6 Thu, 22.02.2018 Feed Forward Networks: Part II here
7 Tue, 27.02.2018
8 Thu, 01.03.2018
9 Tue, 06.03.2018
10 Thu, 08.03.2018
11 Tue, 13.03.2018
12 Thu, 15.03.2018
13 Tue, 20.03.2018
14 Thu, 22.03.2018


Mode of the Exercises

You will receive in total 3 mini-projects. You are supposed to work in teams of up to 4 people on the projects and solve them within two weeks. After one week, you will have a Q&A session with your TA. THe week after, please send your solutions to the TA before the exercises session. During the exercise session you will discuss the solutions together with the TA and the next mini-project is handed out.

The first exercise session (14.1) will be a general Introduction to Theano and is not part of the mini-projects.

# Date Questions Download Solutions
1 Wed, 14.02.2018 Small Introduction to Theano -- --
2 Wed, 21.02.2018 Logistic Regression in Theano: Q&A session Project Description
3 Wed, 28.02.2018 Logistic Regression in Theano: Discussion Solutions --
4 Wed, 07.03.2018
5 Wed, 14.03.2018
6 Wed, 21.03.2018


  • All lecture slides are relevant for the exams.
  • All readings noted in the lecture list are relevant for the exam.
  • The Deep Learning Book