Module – 4 Bayesian Learning: Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting probabilities, MDL principle, Naive Bayes classifier, Bayesian belief networks, EM algorithm

Text book 1, Sections: 6.1 – 6.6, 6.9, 6.11, 6.12

 

Module 4 Notes

Module 4 PPT

Module 4 QB

Module 3

Module – 3 Artificial Neural Networks: Introduction, Neural Network representation, Appropriate problems, Perceptrons, Backpropagation algorithm.

Text book 1, Sections: 4.1 – 4.6

 

Module 3 Notes

Module 3 PPT

Module 3 QB