Course Content (Syllabus)
Introduction, Concept, Learning, Decision Trees, Rule Learning/Inductive Logic Programming, Instance - Based Learning, Bayesian Learning, Learning with Genetic Algorithms, Model Evaluation, Clustering, Association Rules, Feature Selection and Discretization, Ensemble Methods, Reinforcement Learning, Fuzzy Learning, Text Mining, Mining Biological Data, Temporal Data Mining, Machine Learning Software.
Machine Learning, Supervised learning, Unsupervised Learning, Classification, Regression
Additional bibliography for study
- Machine Learning, T. Mitchell, McGraw Hill, 1997
- Introduction to Machine Learning. Ethem Alpaydin, The MIT Press, October 2004.
- Introduction to Data Mining. Pang-Ning Tan, Michael Steinbach and Vipin Kumar. Pearson Addison Wesley. 2005
- Data Mining, Practical Machine Learning Tools and Techniques with Java Implementation (second edition), Ian Witten & Eibe Frank, Morgan Kaufmann, 2005.
- Introduction to Machine Learning (Draft of Incomplete Notes), Nils J. Nilsson, 2015