Introduction to Machine Learning

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)