The course web site has moved to Noppa:
Please find below the archived 2007 course web site.
Lecturer: Kai Puolamäki, lecturing researcher
Contents: The topics include the background principles needed to understand and apply the models of machine learning. After the course, the student is able to apply the basic methods to data and understand new models based on these principles.
Requirements: Examination and exercise work.
Literature: Alpaydin, 2004. Introduction to Machine Learning. The MIT Press; lecture notes.
Prerequisites: Basic mathematics and probability courses; T-106.1200/1203/1206/1207 and T-106.1220/1223.
Additional information: Replaces course T-61.3030 Principles of Neural Computing.
Homepage of the 2008 course:
You should know or be able to independently learn during the course the basics of some data analysis software, such as R, S, Octave or Matlab.
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