NOC:Introduction to Machine Learning


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