Valid from: Spring 2020
Decided by: Professor Thomas Johansson
Date of establishment: 2021-02-02
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 1-9 in the course book.
Knowledge and Understanding
For a passing grade the doctoral student must show that he/she understands the basic concepts and methods presented in Chapters 1-9 in the course book. This includes programming in Python, classification, training models, SVMs, Decision Trees, Ensemble learning and Random Forests, Dimensionality Reduction and Unsupervised Learning Techniques.
Competences and Skills
For a passing grade the doctoral student must participate actively in the discussions on the different chapters, host a chapter-session and complete a project, which includes hands-on implementation of ML-algorithms in Python.
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.
Software setup for Machine Learning (Python), classification, training models, SVMs, Decision Trees, Ensemble learning and Random Forests, Dimensionality Reduction and Unsupervised Learning Techniques.
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.