Introduction to Machine Learning

Overview

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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/PhDPREREQUISITES : 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.INDUSTRY SUPPORT : Any company in the data analytics/data science/big data domain would value this course.

Syllabus

Week 0: Probability Theory, Linear Algebra, Convex Optimization - (Recap)Week 1: Introduction: Statistical Decision Theory - Regression, Classification, Bias VarianceWeek 2:Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squaresWeek 3: Linear Classification, Logistic Regression, Linear Discriminant AnalysisWeek 4: Perceptron, Support Vector MachinesWeek 5: Neural Networks - Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation - MLE, MAP, Bayesian EstimationWeek 6:Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees - Instability Evaluation MeasuresWeek 7: Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods - Bagging, Committee Machines and Stacking, BoostingWeek 8: Gradient Boosting, Random Forests, Multi-class Classification, Naive Bayes, Bayesian NetworksWeek 9: Undirected Graphical Models, HMM, Variable Elimination, Belief PropagationWeek 10: Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based ClusteringWeek 11: Gaussian Mixture Models, Expectation MaximizationWeek 12: Learning Theory, Introduction to Reinforcement Learning, Optional videos (RL framework, TD learning, Solution Methods, Applications)