Subject Code: ID6L004

Name: Machine Learning and Data Analytics-II

L-T-P: 3-0-0

Credit: 3

Prerequisite: None

Regression: Logistic Regression, Sparse Multi-Normal Regression, and Ridge Regression.
Probability-based Learning: Nearest Neighbor Methods; Bayesian Classification; Naïve Bayes; Bayesian and Markov Networks; Hidden Markov Model; Markov Random Fields; EM Algorithm; Probabilistic inference – Metropolis-Hastings Algorithm, Gibbs Sampling
Topic Models: PLSI, Latent Dirichlet Allocation, HMM-LDA, modern variants
Online Algorithms: Online Clustering, online learning, Frequent Itemset mining on streaming data
Reinforcement Learning: Markov Decision Processes, and Q-Learning.
Learning Theory: PAC Learning, Sample Complexity and VC Dimension, and Structural Risk Minimization.
Fuzzy Logic: Tagaki-Sugeno Fuzzy Logic;, Mamdani Fuzzy Logic, Fuzzy Bayesian Decision Method, Membership Functions, Fuzzification and Defuzzification, Fuzzy system Modeling.
Distributed Machine Learning: Incremental and Diffusion Learning Algorithms, Distributed Clustering, Robust Prediction and Classification.
Applications

1.

Bishop, C., Pattern Recognition and Machine Learning, Springer, 2006.

2.

Murphy, K., Machine Learning: A Probabilistic Perspective, MIT Press, 2012.

3.

Koller D. and Friedman N. : Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009

4.

Simon H., Neural Networks and Learning Machines Prentice Hall, Third Edition, 2008.

5.

Timothy J. Ross, Fuzzy Logic with Engineering Applications, John Wiley & Sons, 2010.

6.

Montgomery, D. C., and G. C. Runger, Applied Statistics and Probability for Engineers. John Wiley & Sons, Sixth Edition, 2013.

7.

Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.

8.

NPTEL lectures on Introduction to Machine Learning