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
http://www.cis.hut.fi/Opinnot/T61.6020/
for current information.
Courses in previous years: [ 2000  2001  2002  2003  2004  2005  2006 ]
Instructor  Kai Puolamäki, PhD 

Course assistant  Mikko Korpela, MSc 
Course format  Seminar course 
Credits (ECTS)  5 
Semester  Spring 2007 (during periods III and IV) 
Seminar sessions  On Mondays at 14–16 in lecture
hall T4 in the computer science building Konemiehentie 2, Otaniemi, Espoo 
Language  English 
Web  http://www.cis.hut.fi/Opinnot/T61.6020/ 
t616020@cis.hut.fi 
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.
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):
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  MariSanna Paukkeri  Slides (PDF)  
Apr 30  Combining Models  Jukka Parviainen  Slides (PDF) 
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