Department of Computer Science & Engineering

Syllabus:

Basic Maths : Probability, Linear Algebra, Convex Optimization
Background: Statistical Decision Theory, Bayesian Learning (ML, MAP, Bayes estimates, Conjugate priors)
Regression : Linear Regression, Ridge Regression, Lasso
Dimensionality Reduction : Principal Component Analysis, Partial Least Squares
Classification : Linear Classification, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Perceptron, Support Vector Machines + Kernels, Artificial Neural Networks + BackPropagation, Decision Trees, Bayes Optimal Classifier, Naive Bayes.
Evaluation measures : Hypothesis testing, Ensemble Methods, Bagging Adaboost Gradient Boosting, Clustering, K-means, K-medoids, Density-based Hierarchical, Spectral
Miscellaneous topics: Expectation Maximization, GMMs, Learning theory Intro to Reinforcement Learning
Graphical Models: Bayesian Networks.