Unit 1

Support Vector Machines: Hyperplane, Maximum Margin Classifier, Support Vector Classifiers, Support Vector Machines, One vs One Classification and One vs All Classification, Relationship to Logistic Regression.

Unit 2

Dimensionality reduction, linear methods including PCA, Linear discriminant analysis, Nonlinear methods, Isomap, Local linear embedding, nonlinear PCA, t-SNE

Unit 3

Regression trees, Classification trees, comparison of trees and linear models, Bagging, Random Forests, Boosting.

Unit 4

Bayes Theorem, Prior, Likelihood function, Maximum likelihood estimation, Undirected graphical models, Hidden Markov Models.

Course outcomes
CO1: Understand to apply Logistic regressions.
CO2: Linear discriminant analysis, Nonlinear methods, Isomap, Local linear embedding
CO3: Able to apply Regression trees, Classification trees, comparison of trees and linear models,

Textbooks:

1. G. James, R. Tibshirani, An Introduction to Statistical Learning: with applications in R, Springer.
2. T. Hastie, R. Tibshirani, Elements of Statistical Learning: Data mining, Inference and Predicton, Springer.

References:

• Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press.