Modules / Lectures
Module NameDownloadDescriptionDownload Size
Linear RegressionLinear AlgebraLinear Algebra Tutorial192 kb


New Assignments
Module NameDownload
noc20_cs29_assigment_1noc20_cs29_assigment_1
noc20_cs29_assigment_10noc20_cs29_assigment_10
noc20_cs29_assigment_11noc20_cs29_assigment_11
noc20_cs29_assigment_12noc20_cs29_assigment_12
noc20_cs29_assigment_13noc20_cs29_assigment_13
noc20_cs29_assigment_2noc20_cs29_assigment_2
noc20_cs29_assigment_3noc20_cs29_assigment_3
noc20_cs29_assigment_4noc20_cs29_assigment_4
noc20_cs29_assigment_5noc20_cs29_assigment_5
noc20_cs29_assigment_6noc20_cs29_assigment_6
noc20_cs29_assigment_7noc20_cs29_assigment_7
noc20_cs29_assigment_8noc20_cs29_assigment_8
noc20_cs29_assigment_9noc20_cs29_assigment_9


Sl.No Chapter Name MP4 Download
1A brief introduction to machine learningDownload
2Supervised LearningDownload
3Unsupervised LearningDownload
4Reinforcement LearningDownload
5Probability Basics - 1Download
6Probability Basics - 2Download
7Linear Algebra - 1Download
8Linear Algebra - 2Download
9Statistical Decision Theory - RegressionDownload
10Statistical Decision Theory - ClassificationDownload
11Bias-VarianceDownload
12Linear RegressionDownload
13Multivariate RegressionDownload
14Subset Selection 1Download
15Subset Selection 2Download
16Shrinkage MethodsDownload
17Principal Components RegressionDownload
18Partial Least SquaresDownload
19Linear ClassificationDownload
20Logistic RegressionDownload
21Linear Discriminant Analysis 1Download
22Linear Discriminant Analysis 2Download
23Linear Discriminant Analysis 3Download
24OptimizationDownload
25Perceptron LearningDownload
26SVM - FormulationDownload
27SVM - Interpretation & AnalysisDownload
28SVMs for Linearly Non Separable DataDownload
29SVM KernelsDownload
30SVM - Hinge Loss FormulationDownload
31Weka TutorialDownload
32Early ModelsDownload
33Backpropogation IDownload
34Backpropogation IIDownload
35Initialization, Training & ValidationDownload
36Maximum Likelihood EstimateDownload
37Priors & MAP EstimateDownload
38Bayesian Parameter Estimation Download
39IntroductionDownload
40Regression TreesDownload
41Stopping Criteria & PruningDownload
42Loss Functions for ClassificationDownload
43Categorical AttributesDownload
44Multiway SplitsDownload
45Missing Values, Imputation & Surrogate SplitsDownload
46Instability, Smoothness & Repeated SubtreesDownload
47TutorialDownload
48Evaluation Measures IDownload
49Bootstrapping & Cross ValidationDownload
502 Class Evaluation MeasuresDownload
51The ROC CurveDownload
52Minimum Description Length & Exploratory AnalysisDownload
53Introduction to Hypothesis TestingDownload
54Basic ConceptsDownload
55Sampling Distributions & the Z TestDownload
56Student\'s t-testDownload
57The Two Sample & Paired Sample t-testsDownload
58Confidence Intervals Download
59Bagging, Committee Machines & StackingDownload
60BoostingDownload
61Gradient BoostingDownload
62Random ForestDownload
63Naive Bayes Download
64Bayesian NetworksDownload
65Undirected Graphical Models - IntroductionDownload
66Undirected Graphical Models - Potential FunctionsDownload
67Hidden Markov ModelsDownload
68Variable EliminationDownload
69Belief PropagationDownload
70Partitional ClusteringDownload
71Hierarchical ClusteringDownload
72Threshold GraphsDownload
73The BIRCH AlgorithmDownload
74The CURE AlgorithmDownload
75Density Based ClusteringDownload
76Gaussian Mixture ModelsDownload
77Expectation MaximizationDownload
78Expectation Maximization ContinuedDownload
79Spectral ClusteringDownload
80Learning TheoryDownload
81Frequent Itemset MiningDownload
82The Apriori PropertyDownload
83Introduction to Reinforcement LearningDownload
84RL Framework and TD LearningDownload
85Solution Methods & ApplicationsDownload
86Multi-class ClassificationDownload

Sl.No Chapter Name English
1A brief introduction to machine learningDownload
Verified
2Supervised LearningDownload
Verified
3Unsupervised LearningDownload
Verified
4Reinforcement LearningDownload
Verified
5Probability Basics - 1Download
Verified
6Probability Basics - 2PDF unavailable
7Linear Algebra - 1PDF unavailable
8Linear Algebra - 2PDF unavailable
9Statistical Decision Theory - RegressionPDF unavailable
10Statistical Decision Theory - ClassificationPDF unavailable
11Bias-VariancePDF unavailable
12Linear RegressionPDF unavailable
13Multivariate RegressionPDF unavailable
14Subset Selection 1PDF unavailable
15Subset Selection 2PDF unavailable
16Shrinkage MethodsPDF unavailable
17Principal Components RegressionPDF unavailable
18Partial Least SquaresPDF unavailable
19Linear ClassificationPDF unavailable
20Logistic RegressionPDF unavailable
21Linear Discriminant Analysis 1PDF unavailable
22Linear Discriminant Analysis 2PDF unavailable
23Linear Discriminant Analysis 3PDF unavailable
24OptimizationPDF unavailable
25Perceptron LearningPDF unavailable
26SVM - FormulationPDF unavailable
27SVM - Interpretation & AnalysisPDF unavailable
28SVMs for Linearly Non Separable DataPDF unavailable
29SVM KernelsPDF unavailable
30SVM - Hinge Loss FormulationPDF unavailable
31Weka TutorialPDF unavailable
32Early ModelsPDF unavailable
33Backpropogation IPDF unavailable
34Backpropogation IIPDF unavailable
35Initialization, Training & ValidationPDF unavailable
36Maximum Likelihood EstimatePDF unavailable
37Priors & MAP EstimatePDF unavailable
38Bayesian Parameter Estimation PDF unavailable
39IntroductionPDF unavailable
40Regression TreesPDF unavailable
41Stopping Criteria & PruningPDF unavailable
42Loss Functions for ClassificationPDF unavailable
43Categorical AttributesPDF unavailable
44Multiway SplitsPDF unavailable
45Missing Values, Imputation & Surrogate SplitsPDF unavailable
46Instability, Smoothness & Repeated SubtreesPDF unavailable
47TutorialPDF unavailable
48Evaluation Measures IPDF unavailable
49Bootstrapping & Cross ValidationPDF unavailable
502 Class Evaluation MeasuresPDF unavailable
51The ROC CurvePDF unavailable
52Minimum Description Length & Exploratory AnalysisPDF unavailable
53Introduction to Hypothesis TestingPDF unavailable
54Basic ConceptsPDF unavailable
55Sampling Distributions & the Z TestPDF unavailable
56Student\'s t-testPDF unavailable
57The Two Sample & Paired Sample t-testsPDF unavailable
58Confidence Intervals PDF unavailable
59Bagging, Committee Machines & StackingPDF unavailable
60BoostingPDF unavailable
61Gradient BoostingPDF unavailable
62Random ForestPDF unavailable
63Naive Bayes PDF unavailable
64Bayesian NetworksPDF unavailable
65Undirected Graphical Models - IntroductionPDF unavailable
66Undirected Graphical Models - Potential FunctionsPDF unavailable
67Hidden Markov ModelsPDF unavailable
68Variable EliminationPDF unavailable
69Belief PropagationPDF unavailable
70Partitional ClusteringPDF unavailable
71Hierarchical ClusteringPDF unavailable
72Threshold GraphsPDF unavailable
73The BIRCH AlgorithmPDF unavailable
74The CURE AlgorithmPDF unavailable
75Density Based ClusteringPDF unavailable
76Gaussian Mixture ModelsPDF unavailable
77Expectation MaximizationPDF unavailable
78Expectation Maximization ContinuedPDF unavailable
79Spectral ClusteringPDF unavailable
80Learning TheoryPDF unavailable
81Frequent Itemset MiningPDF unavailable
82The Apriori PropertyPDF unavailable
83Introduction to Reinforcement LearningPDF unavailable
84RL Framework and TD LearningPDF unavailable
85Solution Methods & ApplicationsPDF unavailable
86Multi-class ClassificationPDF unavailable


Sl.No Language Book link
1EnglishNot Available
2BengaliNot Available
3GujaratiNot Available
4HindiNot Available
5KannadaNot Available
6MalayalamNot Available
7MarathiNot Available
8TamilNot Available
9TeluguNot Available