TIES4450 Data Mining and Machine Learning (5 cr)
The course introduces the methods of data mining for large and incomplete data masses. The course starts with the basic concepts, definitions and challenges related to the development and application of data mining methods. Learn about the various stages of the process of knowledge search (KDD) and the methods applied in them. The methods used for different types of data mining problems are reviewed. Get acquainted with the application of methods in practical materials. Exercises and project work are done using Matlab software.
Understanding of the main machine learning / data mining methods and algorithms; knowledge of the basic concepts related to the overall knowledge discovery process
Description of prerequisites
Basic programming skills and knowledge of mathematical and algorithmic concepts are required.
M. J. Zaki and W. Meira Jr.: “Data Mining and Analysis – Fundamental Concepts and Algorithms”, Cambridge University Press, 2014
Hand, Manilla and Smyth: “Principles of Data Mining”, MIT Press
Hastie, Tibshirani and Friedman: “The Elements of Statistical Learning”, Springer, 2017
Principles of Data Mining, D. Hand, H. Mannila, and P. Smyth, MIT Press, 2001. P-N.
Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison Wesley, 2005. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2006. Wang, X.
Participation in teaching (5 cr)
Exam, exercises and project work