WASP AI Deep learning VT23
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
Links to an external site. 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.