Course Syllabus for

Hands-on Machine Learning III
Tillämpad maskininlärning III

FRT250F, 3 credits

Valid from: Autumn 2020
Decided by: Professor Thomas Johansson
Date of establishment: 2021-03-01

General Information

Division: Automatic Control
Course type: Third-cycle course
Teaching language: English


To demonstrate understanding of the concepts and show the ability to use the methods of Chapters 14-17 in the course book.


Knowledge and Understanding

For a passing grade the doctoral student must demonstrate understanding for the basic concepts and methods presented in Chapters 14-17 in the course book. This includes programming in Python, CNNs, RNNs, Autoencoders and GANs.

Competences and Skills

For a passing grade the doctoral student must

Judgement and Approach

For a passing grade the doctoral student must demonstrate understanding for the suitability of different ML-algorithms to bench-mark problems and be able to choose parts of the chapter content to present at the chapter-session they are hosting as well as organize the meeting.

Course Contents

Deep computer vision using convolutional neural networks, processing sequences using RNNs and CNNs, natural language processing with RNNs and attention, representation, generation using autoencoders and GANs.

Course Literature

Géron, A.: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Unsupervised learning techniques. 2019. ISBN 9781492032649.

Instruction Details

Types of instruction: Seminars, project, self-study literature review

Examination Details

Examination format: Seminars given by participants. Sufficient participation in the seminars and the discussions.
Grading scale: Failed, pass

Admission Details

Selection criteria: None

Further Information

The course is given upon request, if sufficient demand is present.

Course Occasion Information

Contact and Other Information

Course coordinators:

Web page:

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