English

If sufficient demand

To demonstrate understanding of the concepts and show the ability to use the methods of Chapters 10-13 and 18 in the course book.

Introduction to artificial neural networks with Keras, training of deep neural networks, loading and preprocessing of data with TensorFlow, and reinforcement learning.

demonstrate understands for the basic concepts and methods presented in Chapters 10-13 and 18 in the course book. This includes programming in Python, artificial neural networks with Keras, training av deep neural networks, loading and preprocessing of data with TensorFlow, and reinforcement learning.

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, possibly using Keras and/or TensorFlow.

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.

FRT245F

2021-03-01

Professor Thomas Johansson