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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PAC Learning

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Probably Approximately Correct (PAC) learning framework. PAC (Probably Approximately Correct) learning is a framework used for mathematical analysis. A PAC Learner tries to learn a concept (approximately correct) by selecting a hypothesis from a set of hypotheses that has a low generalization error. In the context of Machine Learning, a problem is PAC-learnable if there is an algorithm $A$ when given some independently drawn samples, will produce a hypothesis with a small error for any distribution $D$ and any concept $c$, and that too with a high probability. It may not be possible to find a perfect hypothesis with zero error so the goal is to find a consistent hypothesis that can predict approximately correctly with an upper bound on the error.The PAC learning framework was introduced by Valiant [1984]. Motivation   Some computational learning questions  • What can be learned efficiently?  • What is inherently hard to learn?  • A general model of learning? ...
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VC(Vapnik-Chervonenkis)Dimension

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