Modules / Lectures
Module NameDownloadDescriptionDownload Size
Linear RegressionLinear AlgebraLinear Algebra Tutorial192 kb
Module NameDownloadDescriptionDownload Size
Probability TheoryAssignment 1Assignment 1 with Solutions187 kb
Probability TheoryAssignment 1Assignment 1 Questions170 kb
Linear RegressionAssignment 2Assignment2 with Solutions147 kb
Linear RegressionAssignment 2Assignment 2 Questions123 kb
Classification - Linear ModelsAssignment 3Assignment 3 with Solutions167 kb
Classification - Linear ModelsAssignment 3Assignment 3 Questions112 kb
Classification - Separating Hyperplane ApproachesAssignment 4Assignment 4 with Solutions134 kb
Classification - Separating Hyperplane ApproachesAssignment 4Assignment 4 Questions127 kb
Parameter EstimationAssignment 5Assignment 5 Questions154 kb
Parameter EstimationAssignment 5Assignment 5 Questions142 kb
Evaluation MeasuresAssignment 6Assignment 6 with Solutions259 kb
Evaluation MeasuresAssignment 6Assignment 6 Questions84 kb
Hypothesis TestingAssignment 7Assignment 7 with Solutions98 kb
Hypothesis TestingAssignment7Assignment 7 Questions53 kb
Ensemble MethodsAssignment 8Assignment 8 with Solutions57 kb
Ensemble MethodsAssignment 8Assignment 8 Questions45 kb
Graphical ModelsAssignment 9Assignment 9 with Solutions214 kb
Graphical ModelsAssignment 9Assignment 9 Questions172 kb
ClusteringAssignment 10Assignment 10 with Solutions203 kb
ClusteringAssignment 10Assignment 10 Questions188 kb
Learning TheoryAssignment 11Assignment 11 Questions118 kb
Learning TheoryAssignment 11Assignment 11 with Solutions167 kb
Reinforcement LearningAssignment 12Assignment 12 Questions61 kb
Reinforcement LearningAssignment 12Assignment 12 with Solutions81 kb


Sl.No Chapter Name MP4 Download Transcript Download
1A brief introduction to machine learningDownloadDownload
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2Supervised LearningDownloadDownload
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3Unsupervised LearningDownloadDownload
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4Reinforcement LearningDownloadDownload
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5Probability Basics - 1DownloadDownload
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6Probability Basics - 2DownloadDownload
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7Linear Algebra - 1DownloadDownload
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8Linear Algebra - 2DownloadDownload
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9Statistical Decision Theory - RegressionDownloadDownload
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10Statistical Decision Theory - ClassificationDownloadDownload
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11Bias-VarianceDownloadDownload
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12Linear RegressionDownloadDownload
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13Multivariate RegressionDownloadDownload
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14Subset Selection 1DownloadDownload
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15Subset Selection 2DownloadDownload
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16Shrinkage MethodsDownloadDownload
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17Principal Components RegressionDownloadDownload
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18Partial Least SquaresDownloadDownload
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19Linear ClassificationDownloadDownload
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20Logistic RegressionDownloadDownload
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21Linear Discriminant Analysis 1DownloadDownload
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22Linear Discriminant Analysis 2DownloadDownload
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23Linear Discriminant Analysis 3DownloadDownload
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24OptimizationDownloadDownload
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25Perceptron LearningDownloadDownload
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26SVM - FormulationDownloadDownload
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27SVM - Interpretation & AnalysisDownloadDownload
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28SVMs for Linearly Non Separable DataDownloadDownload
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29SVM KernelsDownloadDownload
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30SVM - Hinge Loss FormulationDownloadDownload
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31Weka TutorialDownloadDownload
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32Early ModelsDownloadDownload
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33Backpropogation IDownloadDownload
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34Backpropogation IIDownloadDownload
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35Initialization, Training & ValidationDownloadDownload
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36Maximum Likelihood EstimateDownloadDownload
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37Priors & MAP EstimateDownloadDownload
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38Bayesian Parameter Estimation DownloadDownload
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39IntroductionDownloadDownload
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40Regression TreesDownloadDownload
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41Stopping Criteria & PruningDownloadDownload
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42Loss Functions for ClassificationDownloadDownload
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43Categorical AttributesDownloadDownload
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44Multiway SplitsDownloadDownload
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45Missing Values, Imputation & Surrogate SplitsDownloadDownload
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46Instability, Smoothness & Repeated SubtreesDownloadDownload
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47TutorialDownloadDownload
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48Evaluation Measures IDownloadDownload
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49Bootstrapping & Cross ValidationDownloadDownload
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502 Class Evaluation MeasuresDownloadDownload
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51The ROC CurveDownloadDownload
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52Minimum Description Length & Exploratory AnalysisDownloadDownload
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53Introduction to Hypothesis TestingDownloadDownload
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54Basic ConceptsDownloadDownload
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55Sampling Distributions & the Z TestDownloadDownload
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56Student\'s t-testDownloadDownload
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57The Two Sample & Paired Sample t-testsDownloadDownload
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58Confidence Intervals DownloadDownload
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59Bagging, Committee Machines & StackingDownloadDownload
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60BoostingDownloadDownload
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61Gradient BoostingDownloadDownload
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62Random ForestDownloadDownload
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63Naive Bayes DownloadDownload
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64Bayesian NetworksDownloadDownload
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65Undirected Graphical Models - IntroductionDownloadDownload
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66Undirected Graphical Models - Potential FunctionsDownloadDownload
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67Hidden Markov ModelsDownloadDownload
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68Variable EliminationDownloadDownload
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69Belief PropagationDownloadDownload
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70Partitional ClusteringDownloadDownload
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71Hierarchical ClusteringDownloadDownload
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72Threshold GraphsDownloadDownload
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73The BIRCH AlgorithmDownloadDownload
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74The CURE AlgorithmDownloadDownload
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75Density Based ClusteringDownloadDownload
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76Gaussian Mixture ModelsDownloadDownload
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77Expectation MaximizationDownloadDownload
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78Expectation Maximization ContinuedDownloadDownload
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79Spectral ClusteringDownloadDownload
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80Learning TheoryDownloadDownload
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81Frequent Itemset MiningDownloadDownload
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82The Apriori PropertyDownloadDownload
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83Introduction to Reinforcement LearningDownloadDownload
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84RL Framework and TD LearningDownloadDownload
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85Solution Methods & ApplicationsDownloadDownload
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86Multi-class ClassificationDownloadDownload
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