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Introduction to Machine Learning
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Lecture 1 - Mathematical Background for Machine Learning
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Annotated Notes Lecture 1
(46 pages)
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Lecture 2 - Fitting the Line
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Annotated Notes Lecture 2
(36 pages)
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Lecture 3 - Linear Regression in d-dimensions (or Multiple Linear Regression)
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Annotated Notes Lecture 3
(23 pages)
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Lecture 4: Linear Regression Continued
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Annotated Notes Lecture 4
(29 pages)
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Lecture 5: Linear Regression Optimisation
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Annotated Notes Lecture 5
(29 pages)
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Lecture 6: Maximum Likelihood Estimate
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Annotated Notes Lecture 6
(27 pages)
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Lecture 7: Maximum Likelihood Estimate Contd.
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Annotated Notes Lecture 7
(28 pages)
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Lecture 8: Linear Regression, MLE and Multiple Linear Regression
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Annotated Notes Lecture 8
(27 pages)
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Lecture 9: Ridge Regression, Logistic Regression
118:00
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Annotated Notes Lecture 9
(34 pages)
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Lecture 10: MAP, Logistic Regression
110:00
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Annotated Notes Lecture 10
(27 pages)
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Lecture 11: Logistic Regression
120:00
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Annotated Notes Lecture 11
(28 pages)
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Lecture 12: Bias Variance Trade-off
122:00
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Annotated Notes Lecture 12 Bias_Variance_Tradeoff
(33 pages)
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Lecture 13: Bias Variance Trade off (contd.)
124:00
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Annotated Notes Lecture 13 Bias_Variance_Tradeoff
(31 pages)
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Lecture 14: Decision Tree
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Annotated Notes Lecture 14 Decision_Tree
(35 pages)
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Lecture 15: Decision Tree (contd.)
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Annotated Notes Lecture 15 Decision_Tree
(35 pages)
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Lecture 16: Ensemble Learning
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Lecture 17 : Introduction to Bayesian Decision Theory
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Annotated Notes Lecture 17 Introduction to Bayesian Decision Theory
(23 pages)
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Lecture 18 : Naive Bayes Classifier - Part-1
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Annotated Notes Lecture 18 Naive Bayes Classifier - Part-1
(22 pages)
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Lecture 19 : Naive Bayes Classifier - Part-2
81:00
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Annotated Notes Lecture 19 Naive Bayes Classifier - Part-2
(16 pages)
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Lecture 20 : Naive Bayes Classifier - Part-3
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Annotated Notes Lecture 20 Naive Bayes Classifier - Part-3
(21 pages)
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Lecture 21 : Introduction to KNN
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Annotated Notes Lecture 21 Introduction to KNN
(18 pages)
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Lecture 22 : K Nearest Neighbour - Part-2
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Annotated Notes Lecture 22 - K Nearest Neighbour - Part - 2
(23 pages)
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Lecture 23: Logistic Regression Revisited
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Annotated Notes Lecture 23 Logistic Regression Revisited
(62 pages)
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Lecture 24: Multiclass Logistic Regression
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Annotated Notes Lecture 24 Multiclass Logistic Regression
(28 pages)
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Lecture 25: Concluding Logistic Regression and Introducing SVM
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Annotated Notes Lecture 25 Multiclass Logistic Regression and SVM
(32 pages)
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Lecture 26: Precision Recall SVM
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Annotated Notes Lecture 26 Precision Recall
(27 pages)
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Lecture 27: Introduction to Linear Discriminant Analysis
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Annotated Notes Lecture 27: Introduction to Linear Discriminant Analysis
(19 pages)
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Lecture 28: Linear Discriminant Analysis Part-2
149:00
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Annotated Notes Lecture 28: Linear Discriminant Analysis Part-2
(27 pages)
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Lecture 29: Prelims of SVM
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Annotated Notes Lecture 29 SVM Basics
(26 pages)
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Lecture 30: SVM Dual
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Annotated Notes Lecture 30 SVM Dual
(21 pages)
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Lecture 31:: Soft-margin SVM
93:00
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Annotated Notes Lecture 31 Soft-margin SVM
(24 pages)
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Stanford Notes SVM
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Lecture 32:: Soft-margin SVM continued
82:00
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Annotated Notes Lecture 32 SVM
(21 pages)
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Lecture 33 Cross Validation
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CMU Slides Cross Validation
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Annotated Notes Lecture 33 Cross Validation
(25 pages)
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Lecture 34: PCA
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Annotated Notes Lecture 34 PCA
(19 pages)
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Lecture 35:- PCA Continued
106:00
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Annotated Notes Lecture 35 PCA Continued
(27 pages)
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Lecture 36:: PCA 2
117:00
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Annotated Notes Lecture 36 PCA 2
(25 pages)
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Lecture 37: PCA from Covariance
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Annotated Notes Lecture 37 PCA from Covariance
(26 pages)
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Lecture 38: PCA from Covariance 2
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Annotated Notes Lecture 38 PCA
(17 pages)
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Lecture 39: Introduction to Perceptron Algorithm
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Annotated Notes Lecture 39: Introduction to perceptron algorithm
(14 pages)
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Lecture 40: Perceptron Algorithm - Part 2
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Annotated Notes Lecture 40: Perceptron Algorithm - Part 2
(16 pages)
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Lecture 41: Introduction to Multilayer Perceptron
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Annotated Notes Lecture 41: Introduction to Multilayer Perceptron
(19 pages)
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Chain Rule in partial differentiation
(3 pages)
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Lecture 42: Feed Forward Neural Networks
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Annotated Notes Lecture 42: Feed Forward Neural Networks
(17 pages)
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Lecture 43: Backpropagation and Representation Power of NN
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Annotated Notes Lecture 43: Backpropagation and Representation Power of NN
(21 pages)
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Lecture 44 : Intro to Cluster Analysis, K Means Clustering
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Annotated Notes Lecture 44 : Intro to Cluster Analysis, K Means Clustering
(13 pages)
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Lecture 45: K Means Clustering Part-2
97:00
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Annotated Notes Lecture 45 : K Means clustering Part-2
(29 pages)
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Lecture 46: Hierarchical Clustering
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Annotated Notes Lecture 46 : Hierarchical Clustering
(22 pages)
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Lecture 47: A Star Algorithm | Sample Paper Questions Solved | 30 Examples
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Annotated Notes - A Star Algorithm
(106 pages)
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Practice Sessions
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Practice Session 1
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Annotated Notes Practice Session Machine Learning 1
(59 pages)
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Practice Session 2
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Annotated Notes Practice Session Machine Learning 2
(50 pages)
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Practice Session 3
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Practice Session 4
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