Advanced Topics in Machine Learning - CST396 KTU CS Sixth Semester Honours Notes - Dr Binu V P 9847390760

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Syllabus

Module-1 (Supervised Learning)

Overview of  Machine Learning

Supervised Learning,Regression

Naive Bayes Classifier

Decision Trees-ID3

Discriminative and Generative Learning Algorithms

Module-2 ( Unsupervised Learning)

Similarity Measures

Clustering-K Means, EM Clustering



Module -3 (Practical aspects in machine learning)



Module -4 (Statistical Learning Theory)

PAC( Probably Approximately Correct) learning

Vapnik-Chervonenkis(VC) dimension.

Module -5 (Advanced Machine Learning Topics)

Graphical Models-Bayesian Belief Networks, Markov Random Fields(MRFs)

Inference in Graphical Models-Inference on Chain , Trees and factor Graphs



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