|Course Code: CS361|
Course Name: Machine Learning
Syllabus: Definitions, goals and history of Machine Learning. Taxonomies of methods and research paradigms. Knowledge-level vs. symbol-level learning. Major approaches of learning: Inductive concept acquisition (version-space, ID3, and AQ algorithms); inductive bias, minimum description length principle. Formal models of learnability. learning in the limit. PAC learnability. Ockham's razor. Learning by observation and discovery (e.g., conceptual clustering in CLUSTER and COBWEB) Scientific and mathematical discovery (e.g., AM and BACON) Explanation-based learning: macro-operators (STRIPS), explanation generalization (as in EBG, EGGS, and SOAR); Connectionist (i.e. neural network) learning (perception and back-propagation), Analogy and case-based reasoning (exemplars, structure mapping).
Texts: 1. J. Shavlik and T. Dietterich (Ed), Readings in Machine Learning, Morgan Kaufmann, 1990.
2. P. Langley, Elements of Machine Learning, Morgan Kaufmann, 1995.