Literature and examination

Literature

In the lecture plan (see HT 2014, Schedule and course plan in the menu to the left), we have stated the chapters and articles that should be read before each lecture.

Text Book

• K. P. Murphy. Machine Learning: a Probabilistic Perspective. MIT Press, 2013.

The book can be ordered in paper format from your favorite internet bookstore or in electronic format from MIT Press, and found using ISBN 978-0262018029.

Articles

• R. M. Everson and J. E. Fieldsend. A variable metric probabilistic k-nearest-neighbour classifier. In Intelligent Data Engineering and Automated Learning (IDEAL), pp 654-659, 2004.
• D. J. C. MacKay. Bayesian model comparison and backprop nets. In Advances in Neural Information Processing Systems (NIPS) 4, pp 839-846, 1991.

Other Resources

There are a large number of video lectures on Machine Learning available. We recommend, e.g.,

• Chris Bishop - Embracing Uncertainty: The new machine intelligence
• Neil Lawrence - What is Machine Learning?
• Iain Murray - Introduction to Machine Learning

To get an idea of state-of-the-art in Machine Learning research and development, take a look at the program of the annual conferences ICML and NIPS.

• ICML 2014
• NIPS 2014

Examination

Assignments

The examination in the course is performed through:

• Three home assignments (4.0 credits). The assignments are performed individually, and presented orally as described in the assignment descriptions. Grade: A - F(fail).
• A project assignment (3.5 credits). The projects are performed in groups of 4-5 students, and presented with a short written report, as well as an oral presentation given by all group members. Grade (normally the same for all group members): A - F(fail).

Details about the assignments themselves can be found under Assignments and Project in the menu.

Grading

The course grade is the weighted average of the assignment grade and the project grade, according to the following: