Valid from: Autumn 2020
Decided by: Professor Thomas Johansson
Date of establishment: 2021-03-01
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.
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.
Géron, A.: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Unsupervised learning techniques. 2019. ISBN 9781492032649.
Types of instruction: Seminars, project, self-study literature review
Examination format: Seminars given by participants.
Sufficient participation in the seminars and the discussions.
Grading scale: Failed, pass
Selection criteria: None
The course is given upon request, if sufficient demand is present.