Computational Learning - VideoAndMovie

Lecture 18 (Evaluation Measures)

1:57:43

Lecture 18 (Evaluation Measures)

Lecture 24 (Deep Learning)

1:28:11

Lecture 24 (Deep Learning)

Lecture 23 (ML) - Part II

57:53

Lecture 23 (ML) - Part II

Lecture 22 (ML)-Part I

1:25:14

Lecture 22 (ML)-Part I

Lecture 17 (CNN)

1:30:22

Lecture 17 (CNN)

Lecture 16 (Hierarchical & Probabilistic Clustering, Association Rule Mining, Apriori Algorithm)

1:41:28

Lecture 16 (Hierarchical & Probabilistic Clustering, Association Rule Mining, Apriori Algorithm)

Lecture 21 (NLP)-Part II

1:28:40

Lecture 21 (NLP)-Part II

Lecture 20 (NLP)-Part I

1:21:15

Lecture 20 (NLP)-Part I

Lecture 15 (Clustering) - Part II

36:42

Lecture 15 (Clustering) - Part II

Lecture 14 (Clustering) -Part I

1:17:53

Lecture 14 (Clustering) -Part I

Lecture 19 (Games) - Part III

26:19

Lecture 19 (Games) - Part III

Lecture 18 (Games) - Part III

50:21

Lecture 18 (Games) - Part III

Lecture 17 (Games)-Part II

11:39

Lecture 17 (Games)-Part II

Lecture 16 (Games)-Part I

1:21:39

Lecture 16 (Games)-Part I

Lecture 15 (Backtracking-II)

52:53

Lecture 15 (Backtracking-II)

Lecture 14 (Backtracking-I)

55:44

Lecture 14 (Backtracking-I)

Lecture 13 (Information Gain, Gain Ratio, Gini Index, Prunning DT and Random Forest)

1:24:24

Lecture 13 (Information Gain, Gain Ratio, Gini Index, Prunning DT and Random Forest)

Lecture 12 (Remaining Activation Functions, Neural Network, And Decision Trees)

1:41:56

Lecture 12 (Remaining Activation Functions, Neural Network, And Decision Trees)

Lecture 13 (Crypto Arithmetic Problems)

41:56

Lecture 13 (Crypto Arithmetic Problems)

Lecture 12 (Genetic III, Constraint Satisfaction, Crypto Arithmetic)

44:44

Lecture 12 (Genetic III, Constraint Satisfaction, Crypto Arithmetic)

Lecture 11 (Genetic Algorithm - Part II)

40:20

Lecture 11 (Genetic Algorithm - Part II)

Lecture 11 (Neural Netowrk and Activation Functions)

48:47

Lecture 11 (Neural Netowrk and Activation Functions)

Lecture 10 (Loss Function and Gradient Descent of Logistic Regression)

1:58:42

Lecture 10 (Loss Function and Gradient Descent of Logistic Regression)

Lecture 9: Hill Climbing

1:20:43

Lecture 9: Hill Climbing

Lecture 10: Simulated Annealing and Genetic Algorithm (part-I)

1:21:12

Lecture 10: Simulated Annealing and Genetic Algorithm (part-I)

Lecture 9 (Supervised Learning Techniques): Logistic Regression

1:09:26

Lecture 9 (Supervised Learning Techniques): Logistic Regression

Lecture 8 (Data Preparation and Feature Selection

55:51

Lecture 8 (Data Preparation and Feature Selection

Lecture 7 (Data Preparation)

47:15

Lecture 7 (Data Preparation)

Lecture 6 (Understanding data through visualization)

51:05

Lecture 6 (Understanding data through visualization)

Lecture 5 (Understanding data through statistics)

42:56

Lecture 5 (Understanding data through statistics)