SUBJECT
Title
Data Mining in Smart Systems
Type of instruction
lecture+practical
Level
Master
Faculty
Part of degree program
Credits
2+2+1
Recommended in
Semester 2
Typically offered in
Spring semester
Course description
Data pre-processing, preparation (missing value imputation, noise handling and outlier detection, data transformation); clustering techniques (k-means, hierarchical, density-based); frequent pattern and association rule mining (Apriori, Eclat, FP-Growth); prediction models (linear and logistic regression, decision trees, SVM, Bayes models, kernels, matrix factorization); building model ensembles (ensembles, bagging, boosting); model evaluation (overfitting, bias-variance, cross-validation).
Readings
Compulsory readings:
- Mohammed J. Zaki and Wagner Meira Jr (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 1st Edition. 562 pages. ISBN-13: 978-0521766333. (http://www.dataminingbook.info/)
- Ethem Alpaydin (2009). Introduction to Machine Learning. The MIT Press, Adaptive Computation and Machine Learning series, 2nd edition. 584 pages. ISBN-13: 978-0262012430.
Recommended readings:
- Ian H. Witten, Eibe Frank and Mark A. Hall (2014). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Series in Data Management Systems, 3rd Edition. 664 pages. SBN-13: 978-0123748560.
- Christopher M. Bishop (2011). Pattern Recognition and Machine Learning. Springer, Information Science and Statistics series. 738 pages. ISBN-13: 978-0387310732.