Helsinki University of Technology
Laboratory of Computer and Information Science

T-61.3050 Machine Learning: Basic Principles (5 cr)
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Autumn 2007

Kai Puolamäki, PhD, lecturer
Antti Ukkonen, MSc, course assistant

Email: t613050@james.hut.fi
Homepage: http://www.cis.hut.fi/Opinnot/T-61.3050/2007/


Contents

The topics of the course include the background principles needed to
understand and apply the models of machine learning. After the course,
the student is able to (i) apply the basic methods to real world data,
(ii) understand the basic principles of the methods and (iii)
understand and apply new concepts and methods that build on the topics
covered in the course.

Machine learning is programming computers to learn or optimize a
performance criterion using example data or past experience. Machine
learning is closely related to data mining and statistics, and also to
the theoretical computer science, especially analysis of algorithms.

Machine learning comes into play when computers have to deal with
natural or otherwise "noisy" data. The course covers the principles of
probabilistic modeling, as well as algorithmic considerations, in data
analysis. The course introduces some basic machine learning methods
that can be used in classification, regression and unsupervised
learning, as well as hands-on applications of these methods and
principles in real data analysis examples.


About the course

Lectures: Tuesdays at 10-12 (from 11 September to 11 December 2007).
Problem sessions: Fridays at 10-12.
Requirements: Examination and exercise work.
Prerequisites: Basic mathematics and probability courses, basics of
               programming, data structures and algorithms.
Literature: Alpaydin, 2004. Introduction to Machine Learning. The MIT
            Press. Lecture notes.
Language: English.


The lectures and problem sessions take place in the lecture hall T1 of
the TKK Computer Science Building (Konemiehentie 2, Espoo). The first
lecture will begin on 11 September 2007 at 10 o'clock.

See the course homepage for further information.