English

If sufficient demand

To demonstrate understanding of the concepts and show the ability to use the methods of Chapters 1-9 in the course book.

Software setup for Machine Learning (Python), classification, training models, SVMs, Decision Trees, Ensemble learning and Random Forests, Dimensionality Reduction and Unsupervised Learning Techniques.

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.

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.

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.

Seminars

Project

Self-study literature review

Seminars given by participants

Sufficient participation in the seminars and the discussions.

Failed, pass

None

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

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

FRT230F

2021-02-02

Professor Thomas Johansson