Course Syllabus

Introduction: Machine learning, Terminologies in machine learning, Types of machine learning: supervised, unsupervised, semi-supervised learning. Review of probability.

Discriminative Models : Least Square Regression, Gradient Descent Algorithm, Univariate and Multivariate Linear Regression, Prediction Model, probabilistic interpretation, Regularization, Logistic regression, multi class classification, Support Vector Machines- Large margin classifiers, Nonlinear SVM, kernel functions, SMO algorithm.

Computational Learning theory- Sample complexity, ε- exhausted version space, PAC Learning, agnostic learner, VC dimensions, Sample complexity – Mistake bounds. Gaussian models: Multivariate Gaussian distributions, Maximum Likelihood Estimate, Inferring parameters, Linear and Quadratic Discriminant Analysis, Mixture models, EM algorithm for clustering and learning with latent variables.

Generative models: k-Nearest Neighbour Classification, Bayesian concept learning, Likelihood, Posterior predictive distribution, beta-binomial model, Naive Bayes classifiers, classifying documents using bag of words. Bayesian Statistics and Frequentist statistics. Directed graphical models (Bayes nets), Conditional independence, Inference.

Dimensionality Reduction, Combining weak learners- AdaBoost.

1. E. Alpaydin, “Introduction to Machine Learning”, PHI, 2005.
2. Tom Mitchell, “Machine Learning”, McGraw Hill, 1997
3. Kevin P. Murphy, “Machine Learning, a probabilistic perspective”, The MIT Press Cambridge, Massachusetts, 2012.
4. Alex Smola and SVN. Viswanathan, “Introduction to Machine Learning”, Cambridge University Press, 2008.
5. Introduction to Machine Learning | Nils J. Nilsson, Stanford University.