Modules / Lectures
Module NameDownloadDescriptionDownload Size
Week 1Week 1 Assignment SolutionWeek 1 Assignment Solution445 kb
Week 2Week 2 Assignment SolutionWeek 2 Assignment Solution670 kb
Week 3Week 3 Assignment SolutionWeek 3 Assignment Solution101 kb
Week 4Week 4 Assignment SolutionWeek 4 Assignment Solution61 kb
Week 5Week 5 Assignment SolutionWeek 5 Assignment Solution1 kb
Week 6Week 6 Assignment SolutionWeek 6 Assignment Solution67 kb
Week 7Week 7 Assignment SolutionWeek 7 Assignment Solution1 kb
Week 8:Week 8 Assignment SolutionWeek 8 Assignment Solution31 kb


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Sl.No Chapter Name MP4 Download
1Lecture 01: IntroductionDownload
2Lecture 02: Different Types of LearningDownload
3Lecture 03: Hypothesis Space and Inductive BiasDownload
4Lecture 04: Evaluation and Cross-ValidationDownload
5Tutorial IDownload
6Lecture 05 : Linear RegressionDownload
7Lecture 06 : Introduction to Decision TreesDownload
8Lecture 07 : Learning Decision TreeDownload
9Lecture 08 : OverfittingDownload
10Lecture 9: Python Exercise on Decision Tree and Linear RegressionDownload
11Tutorial II Download
12Lecture 12: k-Nearest NeighbourDownload
13Lecture 13: Feature SelectionDownload
14Lecture 14: Feature ExtractionDownload
15Lecture 15: Collaborative FilteringDownload
16Lecture 16: Python Exercise on kNN and PCADownload
17Lecture 17: Tutorial IIIDownload
18Lecture 18: Bayesian LearningDownload
19Lecture 19: Naive BayesDownload
20Lecture 20 : Bayesian NetworkDownload
21Lecture 21: Python Exercise on Naive BayesDownload
22Lecture 22: Tutorial IVDownload
23Lecture 23 : Logistic RegressionDownload
24Lecture 24: Introduction Support Vector Machine Download
25Lecture 25: SVM : The Dual FormulationDownload
26Lecture 26: SVM : Maximum Margin with Noise Download
27Lecture 27: Nonlinear SVM and Kernel FunctionDownload
28Lecture 28: SVM : Solution to the Dual ProblemDownload
29Lecture 29: Python Exercise on SVMDownload
30Lecture 30: IntroductionDownload
31Lecture 31: Multilayer Neural Network Download
32Lecture 32 : Neural Network and Backpropagation AlgorithmDownload
33Lecture 33: Deep Neural Network Download
34Lecture 34: Python Exercise on Neural Network Download
35Lecture 35: Tutorial VIDownload
36Lecture 36: Introduction to Computational Learning TheoryDownload
37Lecture 37: Sample Complexity : Finite Hypothesis SpaceDownload
38Lecture 38: VC DimensionDownload
39Lecture 39 : Introduction to Ensembles Download
40Lecture 40 : Bagging and BoostingDownload
41Lecture 41 : Introduction to ClusteringDownload
42Lecture 42 : Kmeans ClusteringDownload
43Lecture 43: Agglomerative Hierarchical ClusteringDownload
44Lecture 44: Python Exercise on kmeans clusteringDownload

Sl.No Chapter Name English
1Lecture 01: IntroductionPDF unavailable
2Lecture 02: Different Types of LearningPDF unavailable
3Lecture 03: Hypothesis Space and Inductive BiasPDF unavailable
4Lecture 04: Evaluation and Cross-ValidationPDF unavailable
5Tutorial IPDF unavailable
6Lecture 05 : Linear RegressionPDF unavailable
7Lecture 06 : Introduction to Decision TreesPDF unavailable
8Lecture 07 : Learning Decision TreePDF unavailable
9Lecture 08 : OverfittingPDF unavailable
10Lecture 9: Python Exercise on Decision Tree and Linear RegressionPDF unavailable
11Tutorial II PDF unavailable
12Lecture 12: k-Nearest NeighbourPDF unavailable
13Lecture 13: Feature SelectionPDF unavailable
14Lecture 14: Feature ExtractionPDF unavailable
15Lecture 15: Collaborative FilteringPDF unavailable
16Lecture 16: Python Exercise on kNN and PCAPDF unavailable
17Lecture 17: Tutorial IIIPDF unavailable
18Lecture 18: Bayesian LearningPDF unavailable
19Lecture 19: Naive BayesPDF unavailable
20Lecture 20 : Bayesian NetworkPDF unavailable
21Lecture 21: Python Exercise on Naive BayesPDF unavailable
22Lecture 22: Tutorial IVPDF unavailable
23Lecture 23 : Logistic RegressionPDF unavailable
24Lecture 24: Introduction Support Vector Machine PDF unavailable
25Lecture 25: SVM : The Dual FormulationPDF unavailable
26Lecture 26: SVM : Maximum Margin with Noise PDF unavailable
27Lecture 27: Nonlinear SVM and Kernel FunctionPDF unavailable
28Lecture 28: SVM : Solution to the Dual ProblemPDF unavailable
29Lecture 29: Python Exercise on SVMPDF unavailable
30Lecture 30: IntroductionPDF unavailable
31Lecture 31: Multilayer Neural Network PDF unavailable
32Lecture 32 : Neural Network and Backpropagation AlgorithmPDF unavailable
33Lecture 33: Deep Neural Network PDF unavailable
34Lecture 34: Python Exercise on Neural Network PDF unavailable
35Lecture 35: Tutorial VIPDF unavailable
36Lecture 36: Introduction to Computational Learning TheoryPDF unavailable
37Lecture 37: Sample Complexity : Finite Hypothesis SpacePDF unavailable
38Lecture 38: VC DimensionPDF unavailable
39Lecture 39 : Introduction to Ensembles PDF unavailable
40Lecture 40 : Bagging and BoostingPDF unavailable
41Lecture 41 : Introduction to ClusteringPDF unavailable
42Lecture 42 : Kmeans ClusteringPDF unavailable
43Lecture 43: Agglomerative Hierarchical ClusteringPDF unavailable
44Lecture 44: Python Exercise on kmeans clusteringPDF unavailable


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