Helsinki University of Technology →
Department of Computer Science and
Laboratory of Computer and Information
Learning: Basic Principles → 2007
- The 2007 course has ended. You can still pass the 2007 course by
examination (if you have not already passed the examination)
before the first lecturing period of Autumn 2008
schelude) and by submitting the term project (if you have not
already passed the term project) as per
on 1 September 2008, at latest (you
can submit the term project earlier, but you not guaranteed to get
the grading of the term project before
1 October 2008). There will be a new course in Autumn 2008 which
may, for example, have a different term project and a different selection
of topics (the course contents will likely remain mostly
the same, though).
T-61.3050 Machine Learning: Basic Principles (5 cr)
26 + 26 (2 + 2)
Kai Puolamäki, PhD, lecturing researcher,
Antti Ukkonen, MSc, course assistant
- In T1 on Tuesdays at 10-12 o'clock (from 11 September to 11
December 2007, no lecture on 30 October).
- Problem sessions:
- In T1 on Fridays at 10-12 o'clock
(from 14 September to 7
December 2007, problem sessions are not every week).
and term project.
mathematics and probability courses (Mat-1.1010,
Mat-1.2600/2620; or equivalent),
basics of programming (T-106.1200/1203/1206/1207 or equivalent),
data structures and algorithms (T-106.1220/1223 or equivalent).
- Alpaydin, 2004. Introduction
to Machine Learning. The MIT Press. The course will be based
mostly on topics covered in the Alpaydin's book, with some parts left out, and
with minor parts based on
material to be distributed from the course web site. Additionally, there may be
some useful material, such as lecture notes or copies of the slides,
that can be downloaded from the course web site.
- Additional information:
- Replaces course T-61.3030
Principles of Neural Computing.
You must sign in to the course using WebTOPI.
The lectures and problem sessions take place in the lecture hall T1 of the
Computer Science Building (Konemiehentie 2, Espoo) during
the autumn 2007 semester.
The Alpaydin's book can be borrowed
from the TKK library,
or bought, for example, from
vendors listed in
Course poster: PDF,
This course as a part of the studies of
methodological principles or postgraduate studies.
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.
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. Applications include
syntactic pattern recognition, information retrieval and
search engines, user modeling,
bioinformatics and cheminformatics, analysis of economic data,
natural language processing, speech and handwriting recognition, object
recognition in computer vision, game playing and robot locomotion.
These applications are mostly not covered
at this course but
in subsequent courses, for which this course is prerequisite
or recommend knowledge.
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.
The principles are of a little use for the
if he or she cannot apply them to his or her own data, which is why
the course includes hands-on applications of these methods and principles
in real data analysis examples.
After this course, the student should...
- be able to apply the basic methods to real world data;
- understand the basic principles of the methods; and
- have necessary prerequisites to understand and apply new concepts and
methods that build on the topics covered in the course.
For further introduction to the contents of the course, please read the Preface
(Chapter 1) of the Alpaydin's book.
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last updated Wednesday, 18-Jun-2008 09:37:45 EEST