Machine Learning and Data Mining
CUGS CS Review / CUGS CS Core / CUGS CS Advanced / Other
Suggested # of Credits
CUGS students and PhD students in informatics, systems and computer science. The course demands a certain mathematical and practical programming sophistication.
To provide a hands-on introduction to practical machine learning tools and techniques with applications to data mining in addition to providing an understanding of the theory underlying these tools.
Core courses plus some knowledge of statistics and probability.
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 decision trees, decision rules, bayesian learning and related topics, clustering, association rules and instance based learning, rough set techniques, reinforcement learning, data mining techniques, WEKA and ROSETTA machine learning tools, plus more.
Department of Computer Science, Linköping University
Lectures and Labs. Three two day sessions. An intensive lab part and reading done independently. See course schedule.
Succesful completion of the labs.
The following books may be used in addition to other articles:
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
Teacher and Examiner
Dr. Marcin Szczuka (teacher)
Prof. Patrick Doherty (examiner)
None at this time.
None at this time.