Deep Learning and GANs, 6 credits

Course goals

In the first course module, we aim to ensure that all students understand the basic concepts and tools in deep learning.

The second course module addresses that learning from data is becoming increasingly important in many different engineering fields. Models for learning often rely heavily on optimization; training a machine is often equivalent solving a specific optimization problem. These problems are typically of large-scale. In the second module, we will learn how to solve such problems efficiently.

The third course module contains research-oriented topics, knowledge of which will be useful in various PhD projects within WASP. This module contains five different topics.


The course is offered in spring 2019 and organized into three different modules:


Course module 1: Deep Learning

April 24-25, Chalmers
Lennart Svensson

Prerequisites: Anyone  admitted to WASP-AI should be able to attend this module. However, students with a strong background in Python, machine learning, neural networks and convolutional neural networks are likely to find the module very easy, whereas other students will have a harder time, but hopefully also learn a lot from the experience.

Update: A registration form has been sent out to all WASP PhD’s by e-mail. Last day to answer is Monday 11th of February.

Course module 2: Optimization for Learning

May 20-21, Lund University
Pontus Giselsson

Prerequisites: Basic knowledge in (convex) optimization and machine learning. The students should know different machine learning models that are trained by solving convex problems (linear regression, logistic regression, SVM, empirical risk minimization, etc) as well as deep learning models that are trained via nonconvex optimization.

Program for May 20-21

Course module 3: Deep Learning and GANs

June 4-5, KTH
Hossein Azizpour

Prerequisites: From the theoretical point of view, the course assumes general knowledge about machine learning, especially core deep learning methods and optimization as well as basic understanding of probability theory and calculus. Furthermore, for the course practicals, basic-to-intermediate knowledge of python is required. Familiarity with deep networks packages TensorFlow and PyTorch will be advantageous.


To pass the course all modules have to be completed.