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 to 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 three diﬀerent topics.

Organization

The course is offered in spring 2023 and organized into three different modules:

Module 1: 28-29 March, Chalmers
Module 2: 24-25 April, Lund University
Module 3: 29-30 May, KTH

Note: Please fill out this form related to Module 1 as soon as possible.

Canvas access

If you have registered for the course, you should get a Canvas account in the coming days. The course page and most material are open for all, but to hand in assignments and take quizzes, you need to be able to log in.

If you are an external user (non-KTH), use this URL to log in: https://kth.instructure.com/login/canvas. If you don't know your password, you can get a new one by clicking "Glömt Lösenord" on that page.

Course module 1: Deep Learning

March 28-29, Chalmers
Lennart Svensson

Note that you can find all the material related to module 1 under Modules. In particular, a detailed description of the first module and what is expected from you is available in Module 1.

Course module 2: Optimization for Learning

April 24-25, Lund University
Pontus Giselsson

Information about Module 2 can be found here.

Course module 3: Advanced Topics of Deep Learning

May 29-30, KTH
Hossein Azizpour

Information about Module 3 can be found here.

Examination

To pass the course, all modules have to be completed.