Introduction to Machine Learning Methods for Data Mining
Lectures: 20h
Labs: 20h
Recommended for
All PhD students in informatics, systems and computer science.
The course demands a certain mathematical and practical programming
sophistication.
This course is being given for the first time.
Goals
To provide a hands-on introduction to practical machine learning tools and techniques
with applications to data mining.
Organization
Lectures and labs.
Contents
The course course will consist of introductory seminars on various practical
machine learning tools and techniques and their theoretical underpinnings.
The course is intended to be lab intensive in the sense that each of the
techniques considered will be followed by exercises and labs using appropriate
software tools.
Topics include data mining and machine learning. Algorithmic techniques covered
include statistical modeling, decision trees, covering algorithms, mining
association rules, rough set based techniques. Other topics include
decision rules, classification rules, instance-based learning, clustering,
concept learning, and possibly Bayesian learning, PAC learnability and
reinforcement learning.
Literature
Witten, Ian & Frank Ebe (2000).
Data Mining, Practical Machine Learning Tools
and Techniques with Java Implementations
Morgan Kaufmann Publishers
ISBN 1-55860-552-5
In addition, we may also use the following book as reference literature or as
a second course book:
Mitchell, Tom (1997).
Machine Learning
WCB McGraw-Hill
ISBN 0-07-042807-7
Teachers
Dr. Marcin Szczuka and Professor Patrick Doherty. Dr. Szczuka is a
guest researcher at AIICS and will give the course.
Examiner
Patrick Doherty
Schedule
Fall 2001. Starting in October
Examination
Completion of a lab series (and possibily a written exam if required).
Credit
4 credits.