Sl.No Chapter Name MP4 Download
11.1 Paradigms of Machine LearningDownload
21.2 Few more examplesDownload
31.3 Types of LearningDownload
41.4 Types of supervised learningDownload
51.5 Introduction to RegressionDownload
61.6 Linear regressionDownload
71.7 Geometrical InterpretationDownload
81.8 Iterative solution: Gradient descentDownload
91.9 Gradient DescentDownload
101.10 Choosing Step sizeDownload
111.11 Taylor SeriesDownload
121.12 Stochastic Gradient Descent and basis functionsDownload
131.13 Regularization TechniquesDownload
141.14 Visual Guide to Orthogonal ProjectionDownload
152.1 Binary ClassificationDownload
162.2 K-Nearest Neighbour ClassificationDownload
172.3 Distance metric and Cross-ValidationDownload
182.4 Computational efficiency of KNNDownload
192.5 Introduction to Decision TreesDownload
202.6 Level splittingDownload
212.7 Measure of ImpurityDownload
222.8 Entropy and Information GainDownload
232.9 Generative vs Discriminative modelsDownload
242.10 Naive Bayes classifierDownload
252.11 Conditional IndependenceDownload
262.12 Classifying the test point and summaryDownload
273.1 Discriminative modelsDownload
283.2 Logistic RegressionDownload
293.3 Summary and big pictureDownload
303.4 Maximum likelihood estimationDownload
313.5 Linear separabilityDownload
323.6 Perceptron and its learning algorithmDownload
333.7 Perceptron : A thing of pastDownload
343.8 Perceptron : A thing of pastDownload
353.9 Optimizing weightsDownload
363.10 Handling OutliersDownload
373.11 Dual FormulationDownload
383.12 Kernel formulationDownload
394.1 Artificial Neural NetworksDownload
404.2 Unsupervised learningDownload
414.3 K-means ClusteringDownload
424.4 LLyod's AlgorithmsDownload
434.5 Convergence and InitializationDownload
444.6 Representation LearningDownload
454.7 Orthogonal ProjectionDownload
464.8 Covariance Matrix and Eigen directionDownload
474.9 PCA and mean centeringDownload
484.10 Concluding remarksDownload

Sl.No Chapter Name English
11.1 Paradigms of Machine LearningPDF unavailable
21.2 Few more examplesPDF unavailable
31.3 Types of LearningPDF unavailable
41.4 Types of supervised learningPDF unavailable
51.5 Introduction to RegressionPDF unavailable
61.6 Linear regressionPDF unavailable
71.7 Geometrical InterpretationPDF unavailable
81.8 Iterative solution: Gradient descentPDF unavailable
91.9 Gradient DescentPDF unavailable
101.10 Choosing Step sizePDF unavailable
111.11 Taylor SeriesPDF unavailable
121.12 Stochastic Gradient Descent and basis functionsPDF unavailable
131.13 Regularization TechniquesPDF unavailable
141.14 Visual Guide to Orthogonal ProjectionPDF unavailable
152.1 Binary ClassificationPDF unavailable
162.2 K-Nearest Neighbour ClassificationPDF unavailable
172.3 Distance metric and Cross-ValidationPDF unavailable
182.4 Computational efficiency of KNNPDF unavailable
192.5 Introduction to Decision TreesPDF unavailable
202.6 Level splittingPDF unavailable
212.7 Measure of ImpurityPDF unavailable
222.8 Entropy and Information GainPDF unavailable
232.9 Generative vs Discriminative modelsPDF unavailable
242.10 Naive Bayes classifierPDF unavailable
252.11 Conditional IndependencePDF unavailable
262.12 Classifying the test point and summaryPDF unavailable
273.1 Discriminative modelsPDF unavailable
283.2 Logistic RegressionPDF unavailable
293.3 Summary and big picturePDF unavailable
303.4 Maximum likelihood estimationPDF unavailable
313.5 Linear separabilityPDF unavailable
323.6 Perceptron and its learning algorithmPDF unavailable
333.7 Perceptron : A thing of pastPDF unavailable
343.8 Perceptron : A thing of pastPDF unavailable
353.9 Optimizing weightsPDF unavailable
363.10 Handling OutliersPDF unavailable
373.11 Dual FormulationPDF unavailable
383.12 Kernel formulationPDF unavailable
394.1 Artificial Neural NetworksPDF unavailable
404.2 Unsupervised learningPDF unavailable
414.3 K-means ClusteringPDF unavailable
424.4 LLyod's AlgorithmsPDF unavailable
434.5 Convergence and InitializationPDF unavailable
444.6 Representation LearningPDF unavailable
454.7 Orthogonal ProjectionPDF unavailable
464.8 Covariance Matrix and Eigen directionPDF unavailable
474.9 PCA and mean centeringPDF unavailable
484.10 Concluding remarksPDF unavailable


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