Department of Information Technology

Deep Learning (DL) PhD course (5+3hp)

Spring 2019

Description

Data is becoming more widely available which opens up for game changing possibilities to teach machines to autonomously analyze, learn, and act based this data without human intervention. The development of these data-driven methods has enabled solutions to previously unsolved problems and are by many considered to be the new electricity of today’s society. The most popular and successful set of methods driving this revolution is called deep learning. Today deep learning methods outperform domain-specific techniques in a broad set of areas ranging from medicine, physics and biology to information technology, engineering science and computer science making. This makes knowledge of deep learning relevant for most scientific fields dealing with data.

The weekly lectures will be focused on theoretical aspects and the mandatory hand-in assignments on implementation of deep learning methods. The course will therefore deal less with particular applications within the field. That is where the optional project comes into the picture, where you can use the foundation provided for an application from your own scientific area.

Contents

  • Basics of machine learning
  • Feed forward neural networks
  • Backpropagation
  • Stochastic gradient descent
  • Dropout
  • Batch normalization
  • Convolutional neural networks
  • Autoencoders and Variational autoencoders

Course Structure

The course gives 5 hp (you can receive an additional 3 hp by carrying out a project).

Examination

The examination consists of three hand-in assignments.

Course literature

If you want a book to read along with the lecutres one good book is
[GBC] Ian Goodfellow, Yoshua Bengio and Aaron Courville Deep Learning, MIT Press, 2016.

For the first lectures, the material is also well covered by the following lecture notes
[LWLS] Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön Lecture notes for the Statistical Machine Learning course

Schedule

The course schedule will is available via Timeedit.

Course level

This is a PhD level course.

Prerequisites

Basic undergraduate courses in linear algebra, statistics, probability, optimization and programming experience in Python, MATLAB or similar.

Sign up

Apply for the course no later than 1st of March, by sending en email to Niklas Wahlström together with the solutions to the following small pre-course assignment. We use the first come, first served principle but if we get more accepted applications than the maximum number of participants we might favor an equal distribution of participants from the different departments. Maximum number of participants is 50.

Related Courses

Probabilistic machine learning.

Teachers

Updated  2019-03-29 09:13:08 by Niklas Wahlström.