Modules / Lectures
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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 Transcript Download
1Lecture 01: IntroductionDownloadDownload
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2Lecture 02: Different Types of LearningDownloadDownload
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3Lecture 03: Hypothesis Space and Inductive BiasDownloadDownload
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4Lecture 04: Evaluation and Cross-ValidationDownloadDownload
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5Tutorial IDownloadDownload
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6Lecture 05 : Linear RegressionDownloadDownload
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7Lecture 06 : Introduction to Decision TreesDownloadDownload
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8Lecture 07 : Learning Decision TreeDownloadDownload
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9Lecture 08 : OverfittingDownloadDownload
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10Lecture 9: Python Exercise on Decision Tree and Linear RegressionDownloadDownload
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11Tutorial II DownloadDownload
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12Lecture 12: k-Nearest NeighbourDownloadDownload
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13Lecture 13: Feature SelectionDownloadDownload
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14Lecture 14: Feature ExtractionDownloadDownload
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15Lecture 15: Collaborative FilteringDownloadDownload
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16Lecture 16: Python Exercise on kNN and PCADownloadDownload
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17Lecture 17: Tutorial IIIDownloadDownload
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18Lecture 18: Bayesian LearningDownloadDownload
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19Lecture 19: Naive BayesDownloadDownload
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20Lecture 20 : Bayesian NetworkDownloadDownload
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21Lecture 21: Python Exercise on Naive BayesDownloadDownload
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22Lecture 22: Tutorial IVDownloadDownload
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23Lecture 23 : Logistic RegressionDownloadDownload
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24Lecture 24: Introduction Support Vector Machine DownloadDownload
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25Lecture 25: SVM : The Dual FormulationDownloadDownload
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26Lecture 26: SVM : Maximum Margin with Noise DownloadDownload
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27Lecture 27: Nonlinear SVM and Kernel FunctionDownloadDownload
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28Lecture 28: SVM : Solution to the Dual ProblemDownloadDownload
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29Lecture 29: Python Exercise on SVMDownloadDownload
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30Lecture 30: IntroductionDownloadDownload
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31Lecture 31: Multilayer Neural Network DownloadDownload
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32Lecture 32 : Neural Network and Backpropagation AlgorithmDownloadDownload
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33Lecture 33: Deep Neural Network DownloadDownload
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34Lecture 34: Python Exercise on Neural Network DownloadDownload
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35Lecture 35: Tutorial VIDownloadDownload
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36Lecture 36: Introduction to Computational Learning TheoryDownloadDownload
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37Lecture 37: Sample Complexity : Finite Hypothesis SpaceDownloadDownload
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38Lecture 38: VC DimensionDownloadDownload
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39Lecture 39 : Introduction to Ensembles DownloadDownload
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40Lecture 40 : Bagging and BoostingDownloadDownload
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41Lecture 41 : Introduction to ClusteringDownloadDownload
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42Lecture 42 : Kmeans ClusteringDownloadDownload
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43Lecture 43: Agglomerative Hierarchical ClusteringDownloadDownload
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44Lecture 44: Python Exercise on kmeans clusteringDownloadDownload
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