Laboratory of Computer and Information Science / Neural Networks Research Centre CIS Lab Helsinki University of Technology

These are the archived web pages of the spring 2007 course. The contents of this course vary yearly. The next course will probably take place in spring 2008. Please see for current information.

Courses in previous years: [ 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 ]

T-61.6020 Special Course in Computer and Information Science II (V, P):
Machine Learning: Basic Principles

Course poster: TXT, PDF

Instructor Kai Puolamäki, PhD
Course assistant Mikko Korpela, MSc
Course formatSeminar course
Credits (ECTS)5
SemesterSpring 2007 (during periods III and IV)
Seminar sessionsOn Mondays at 14–16 in lecture hall T4 in the computer science building
Konemiehentie 2, Otaniemi, Espoo

Course description

The seminar course will provide a comprehensive introduction to the fields of pattern recognition and machine learning. The course is especially suitable for advanced undergraduates and PhD students who want to have (or refresh) a comprehensive and solid overview of these fields.

Requirements for passing the course

To pass the course, you must:

To pass with distinction, you should additionally:

Estimate of time usage (5 cr equals approximately 133 hours of working time for a typical student):

Course material

The course is based on the following book:

Information on how to get the book.


The schedule follows the structure of the course book. At each seminar session, one chapter of the book is presented. Below, you can find the PDF slides of the seminar lectures by the students. (Notice that the presentations below have been prepared and given by the students, who also may or may not have the source files (LaTeX, PowerPoint etc.) for the PDF slides. The lecturer does not have the source files.)

Time Subject Lecturer Material Misc
Jan 15 Organization of the course Kai Puolamäki Slides (PDF)
Jan 22 Probability Distributions Mikko Korpela Slides (PDF) A condensed introduction to the exponential family notation (including Bernoulli, Multinomial, Gaussian etc. distributions) is given by subsection 3.1 of Buntine ECML'02. Rest of the section 3, "Background Theory", covers the EM and variational approaches.
Jan 29 Linear Models for Regression Nicolau Gonçalves Slides (PDF)
Feb 5 Linear Models for Classification Teemu Mutanen
Antti Sorjamaa
Slides 1 (PDF)
Slides 1 with comments (PDF)
Slides 2 (PDF)
Feb 12 Neural Networks Tino Ojala
Marcus Dobrinkat
Slides (PDF)
Feb 19 Kernel Methods Jayaprakash Rajasekharan Slides (PDF)
Feb 26 Sparse Kernel Machines Janne Argillander Slides (PDF)
Mar 5 No lecture (exam period)
Mar 12 Graphical Models Dmitrij Lagutin Slides (PDF)
Mar 19 Mixture Models and EM Yoan Miche Slides (PDF)
Mar 26 Approximate Inference Janne Toivola
Ville Turunen
Slides 1 (PDF)
Slides 2 (PDF)
Apr 2 Sampling Methods Matti Pöllä Slides (PDF)
Apr 9 No lecture (easter break)
Apr 16 Continuous Latent Variables Mikaela Klami Slides (PDF)
Apr 23 Sequential Data Mari-Sanna Paukkeri Slides (PDF)
Apr 30 Combining Models Jukka Parviainen Slides (PDF)

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