Literature, reading list, and examination

Literature 

In the lecture plan (see Schedule and course plan in the menu to the left), we  have listed the chapters and articles associated with (and preferably considered or even read before) each lecture.

The text Book and it's relevant chapters

• C. Bishop. Pattern Recognition and Machine Learning. Springer 2006.

The book can be ordered in paper format from your favorite internet bookstore and found using ISBN 978-0387310732. The book has a website at http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm.

The following parts will be covered (and should be read), see Schedule and course plan for when which part will be covered.

Chapter 1: The entire chapter. 

Chapter 2:  The entire chapter is background but you need to know a lot of this, in particular 2.3 and 2.4.

Chapter 3: The entire chapter, but with emphasis on sections 3.1, 3.3 and 3.4.

Chapter 6: 6.1-6.4

Chapter 8: 8.1-8.3

Chapter 9: 9.1-9.3

Chapter 10: 10.1-10.3.1 except 10.2.2 and 10.2.3.

Chapter 12: focus on sections 12.1-12.3 and 12.4.1

Chapter 13: 13.1, 13.2.1, 13.2.2, 13.2.5, 13.2.6

Articles

MacKay, D. (1992) Bayesian interpolation. Neural Computation 4 (3),415-447.

Hyvärinen, A., and Oja, E. (1999) Independent Component Analysis: A Tutorial. http://cis.legacy.ics.tkk.fi/aapo/papers/IJCNN99_tutorialweb/

Lawrence., N.D. (2005) Probabilistic non-linear principal component analysis with Gaussian process latent variable models. In: The Journal of Machine Learning Research 6, 1783–1816.

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 2015
• NIPS 2015

Examination

Assignments

The examination in the course is performed through:

• Two home assignments (4.0 credits). The assignments are performed individually, and more advanced parts may be presented orally, as will be described in the assignment texts. 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. We will form the groups based on grades on assignments. 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: