CBCGS [2019 - current]

CBGS [2015 - 2018]

Old [2000 - 2014]

## Topics with syllabus and resources

100.00 Introduction to Machine Learning

- What is Machine Learning?, Key Terminology, Types of Machine Learning, Issues in Machine Learning, Application of Machine Learning, How to choose the right algorithm, Steps in developing a Machine Learning Application.

200.00 Learning with Regression

- Linear Regression, Logistic Regression.

300.00 Learning with Trees

- Using Decision Trees, Constructing Decision Trees, Classification and Regression Trees (CART).

400.00 Support Vector Machines (SVM)

- Maximum Margin Linear Separators, Quadratic Programming solution to finding maximum margin separators, Kernels for learning non-linear functions.

500.00 Learning with Classification

- Rule based classification, classification by backpropoagation, Bayesian Belief networks, Hidden Markov Models.

600.00 Dimensionality Reduction

- Dimensionality Reduction Techniques, Principal Component Analysis, Independent Component Analysis.

700.00 Learning with Clustering

- K-means clustering, Hierarchical clustering, Expectation Maximization Algorithm, Supervised learning after clustering, Radial Basis functions.

800.00 Reinforcement Learning

- Introduction, Elements of Reinforcement Learning, Model based learning, Temporal Difference Learning, Generalization, Partially Observable States.