1 Introduction_480p
2 Different Types of Learning_480p
3 Hypothesis Space and Inductive Bias_480p
4 Evaluation and CrossValidation_480p
5 Tutorial I_480p
6 Linear Regression_480p
7 Introduction to Decision Trees_480p
8 Learning Decision Tree_480p
9 Overfitting_480p
10 Python Exercise on Decision Tree and Linear Regression_480p
11Tutorial II_480p
12 kNearest Neighbour_480p
13 Feature Selection_480p
14 Feature Extraction_480p
15 Collaborative Filtering_480p
16 Python Exercise on kNN and PCA_480p
17 Tutorial III_480p
18 Bayesian Learning_480p
19 Naive Bayes_480p
20 Bayesian Network_480p
21 Python Exercise on Naive Bayes_480p
22Tutorial IV_480p
23 Logistic Regression_480p
26 SVM Maximum Margin with Noise_480p
24 Introduction Support Vector Machine_480p
25 SVM The Dual Formulation_480p
27 Nonlinear SVM and Kernel Function_480p
28 SVM Solution to the Dual Problem_480p
29 Python Exercise on SVM_480p
30 Introduction_480p
31 Multilayer Neural Network_480p
32 Neural Network and Backpropagation Algorithm_480p
33 Deep Neural Network_480p
34 Python Exercise on Neural Network_480p
35 Tutorial VI_480p
36 Introduction to Computational Learning Theory_480p
37 Sample Complexity Finite Hypothesis Space_480p
38 VC Dimension_480p
39 Introduction to Ensembles_480p
40 Bagging and Boosting_480p
42 Kmeans Clustering_480p
43 Agglomerative Hierarchical Clustering_480p
44 Python Exercise on kmeans clustering_480p
