With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.
INTENDED AUDIENCE This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD
We will assume that the students know programming for some of the assignments.If the students have done introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic topics in the first two weeks as well.
Any company in the data analytics/data science/big data domain would value this course.
COURSE LAYOUTWeek 0: Probability Theory, Linear Algebra, Convex Optimization - (Recap) Week 1: Introduction: Statistical Decision Theory - Regression, Classification, BiasVariance Week 2: Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares Week 3: Linear Classification, Logistic Regression, Linear DiscriminantAnalysis Week 4: Perceptron, Support Vector Machines Week 5: Neural Networks - Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation - MLE, MAP, Bayesian Estimation Week 6: Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values,Decision Trees - InstabilityEvaluation Measures Week 7: Bootstrapping & Cross Validation, Class Evaluation Measures,ROC curve, MDL,Ensemble Methods - Bagging, Committee Machines and Stacking, Boosting Week 8: Gradient Boosting, Random Forests, Multi-class Classification,Naive Bayes, Bayesian Networks Week 9: Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation Week 10: Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering Week 11: Gaussian Mixture Models, Expectation Maximization Week 12: Learning Theory, Introduction to Reinforcement Learning,Optional videos (RL framework, TD learning,Solution Methods, Applications)