Lecture 1 - Introduction
Lecture 2 - Different Types of Learning
Lecture 3 - Hypothesis Space and Inductive Bias
Lecture 4 - Evaluation and Cross-Validation
Lecture 5 - Tutorial - I
Lecture 6 - Linear Regression
Lecture 7 - Introduction to Decision Trees
Lecture 8 - Learning Decision Tree
Lecture 9 - Overfitting
Lecture 10 - Python Exercise on Decision Tree and Linear Regression
Lecture 11 - Tutorial - II
Lecture 12 - k-Nearest Neighbour
Lecture 13 - Feature Selection
Lecture 14 - Feature Extraction
Lecture 15 - Collaborative Filtering
Lecture 16 - Python Exercise on kNN and PCA
Lecture 17 - Tutorial - III
Lecture 18 - Bayesian Learning
Lecture 19 - Naive Bayes
Lecture 20 - Bayesian Network
Lecture 21 - Python Exercise on Naive Bayes
Lecture 22 - Tutorial - IV
Lecture 23 - Logistic Regression
Lecture 24 - Introduction Support Vector Machine
Lecture 25 - SVM : The Dual Formulation
Lecture 26 - SVM : Maximum Margin with Noise
Lecture 27 - Nonlinear SVM and Kennel Function
Lecture 28 - SVM : Solution to the Dual Problem
Lecture 29 - Python Exercise on SVM
Lecture 30 - Introduction
Lecture 31 - Multilayer Neural Network
Lecture 32 - Neural Network and Backpropagation Algorithm
Lecture 33 - Deep Neural Network
Lecture 34 - Python Exercise on Neural Network
Lecture 35 - Tutorial - VI
Lecture 36 - Introduction to Computational Learning Theory
Lecture 37 - Sample Complexity : Finite Hypothesis Space
Lecture 38 - VC Dimension
Lecture 39 - Introduction to Ensembles
Lecture 40 - Bagging and Boosting
Lecture 41 - Introduction to Clustering
Lecture 42 - Kmeans Clustering
Lecture 43 - Agglomerative Hierarchical Clustering
Lecture 44 - Python Exercise on kmeans clustering
