37:06

Lec 2 || Gantt chart, CPM and PERT

55:45

Lec-1 Aptitude class 18-03-2021

57:40

Revision of Operators

34:48

Lec-13 Interfaces in Java (29-01-2022)

22:08

Project report writing using LaTex

35:46

Lec 23 || Feature Engineering- Feature Selection and Reduction (Lec Date: 12/10/2021)

19:28

Lab 7 || Naive Bayes Implementation on Iris Dataset (Lab Date: 20/10/2021)

37:59

Lec 27 || Naive Bayes Classifier_3 (Continuous-valued attributes) (Lec Date: 19/10/2021)

24:41

Lec 26 || Naive Bayes Classifier_2 (Discrete-values attributes) (Lec Date: 18/10/2021)

45:00

Lec 25 || Naive Bayes Classifier_1 (Lec Date: 14/10/2021)

17:10

Lec 24 || Collaborative filtering based recommendation (Lec Date: 13/10/2021)

55:15

Lec-4 switch statement, while loop in Java (19-10-2021)

26:52

Lec-12 abstract method, abstract class, concrete class (27-01-2022)

21:04

Lab-2 Program based on conditional and looping statement (sum of digits) (20-10-2021)

37:33

Lec-3 Conditional Statements in Java (else-if ladder, nested if-else) (19-10-2021)

48:07

Lec-2 Conditional Statements in Java, Flowchart (14-10-2021)

33:00

Lec-6 Primitive and Reference Types in Java, Introduction to Inheritance (12-01-2022)

50:05

Lec-8 Access Specifiers and Modifiers in Java (compared) (17-01-2022)

56:34

Lec-9 Modifiers in Java, Object class, Constructors in Subclasses (18-01-2022)

51:02

Lec-10 super keyword, method overriding, constructors in inheritance (19-01-2022)

46:36

Lec-11 final keyword, Polymorphism, Dynamic Method Dispatch (20-01-2022)

58:23

Lab-1 Operators in Java demonstrated (13-10-2021)

24:23

Lab-3 Program based on Inheritance (Animal class) (18-01-2022)

46:59

Lec-7 Inheritance in Java, Why no multiple inheritance in Java (15-01-2022)

27:06

Lec-1 Java Installation, Environment Variables Configuration, Hello World Program (12-10-2021)

48:49

Lec-5 static example, Java class libraries, Java Packages (08-01-2022)

18:57

Lec 17 || k-Means Clustering problem using Manhattan Distance (Lec Date: 24/09/2021)

38:59

Lec 16 || k-Means Clustering and solved problems (Lec Date: 23/09/2021)

37:47

Lec 14 || Decision Tree implementation for PlayTennis problem (Lec Date: 20/09/2021)

38:02

Lec 13 || Decision Tree problem based on the Continuous-valued attribute (Lec Date: 17/09/2021)

50:06

Lec 21 || Instance-based Learning, kNN & kNN Classification Numerical (Lec Date: 04/10/2021)

32:27

Lec 22 || kNN Regression Numerical, Curse of Dimensionality (Lec Date: 05/10/21)

28:59

Lab 6 || kNN Classifier Implementation on Iris Dataset (Lab Date: 06/10/2021)

43:33

Lec 20 || Agglomerative Clustering with Complete Linkage (Lec Date: 30/09/2021)

52:07

Lec 12 || Decision Tree problem using SunBurn Dataset (Lec Date: 16/09/2021)

33:08

Lec 18 || Clustering, K-means algorithm example, WCSS (Lec Date: 27/09/2021)

46:42

Lec 19 || Elbow method, Agglomerative Clustering with Single Linkage (Lec Date: 28/09/2021)

35:35

Lab 5 || K-Means implementation on Iris Dataset (Lab Date: 29/09/2021)

49:40

Lec 15 || Issues with Decision Tree, Bias, Variance, Underfitting, Overfitting (Lec Date:21/09/2021)

37:59

Lab 4 || Decision Tree implementation on Iris Dataset (Lab Date: 22/09/2021)

1:00:53

Lec 11 || Solving PlayTennis Decision Tree problem using Entropy measure (Lec Date: 14/09/2021)

47:14

Lec 10 || Introduction to Decision Tree, Entropy and Information Gain (Lec Date: 13/09/2021)

17:07

Lec 9 || Linear Regression_2 (Lec Date: 09/09/2021)

39:35

Lab 3 || Practical No 1 Linear Regression (Lab Date: 08/09/2021)

16:03

Lec 8 || Python Implementation of Linear Regression (Lec Date: 07/09/2021)

27:18

Lec 7 || Simple Linear Regression (Lec Date: 06/09/2021)

50:51

Lec 6 || Confusion Matrix for Multi-classes, K-fold Cross-Validation (Lec Date: 02/09/2021)

34:34

Lab 2 || Implementation of Metrics-Accuracy, Precision, Recall, Confusion Mat (Lab Date: 01/09/2021)

42:21

Lec 5 || Accuracy, Precision, Recall, F-Measure (Lec Date: 31/08/2021)

51:08

Lec 4 || Learning Model, Evaluation Metrics, Confusion Matrix (Lec Date: 30/08/2021)

3:36

Lec 3 || Internship Opportunities for 3rd Year (Lec Date: 27/08/2021)

41:41

Lec 2 || Internship Evaluation Instructions, Hypothesis Space, Inductive Bias (Lec Date: 26/08/2021)

51:37

Lec 1 || Types of Learning (Lec Date: 24/08/2021)

7:36

Lab 1 || ML Lab Instructions (Lab date: 25/08/2021)

25:29

Lec 35 || Semantics of Bayesian/Belief Networks, Monte Carlo Approximation (Lec Date: 14/06/2021)

30:17

Lec 34 || The Wumpus World Revisited (Lec Date: 08/06/2021)

23:03

Lec 33 || Inference Using Full Joint Distributions, Independence, Bayes’ Rule (Lec Date: 07/06/2021)

26:03

Lec 32 || Uncertainty and Basic Probability Notation (Lec Date: 02/06/2021)

13:49

Lec 31 || Unit VI - Uncertainty Introduction (Lec Date: 31/05/2021)

33:34

Lec 30 || Inference in First-order logic_2 (Lec Date: 26/05/2021)

22:02

Lec 29 || Inference in First-order logic_1 (Lec Date: 25/05/2021)

17:32

Lec 28 || Problems on conversion of given statements into FOL statements_2 (Lec Date: 24/05/2021)

48:31

Lec 26 || Revision of Units 1-3 (Lec Date: 12/05/2021)

11:13

Lec 27 || Problems on conversion of given statements into FOL statements_1 (Lec Date: 24/05/2021)

36:30

Lec 25 || First order logic 2 (Lec Date: 10/05/2021)

35:32

Lec 24 || First order logic (Lec Date: 03/05/2021)

30:38

Lec 23 || Horn and Definite Clause, Forward and Backward Chaining (Lec Date: 28/04/2021)

30:48

Lec 22 || Propositional Theorem Proving, Resolution Algorithm (Lec Date: 27/04/2021)

21:57

Lec 21 || Propositional Logic (Lec Date: 26/04/2021)

39:45

Lec 18 || Alpha-beta pruning solved problems (Lec Date: 19/04/2021)

20:10

Lec 19 || Alpha-beta pruning simulator (Lec Date: 20/04/2021)

26:19

Lec 20 || Knowledge-based agents, The Wumpus World (Lec Date: 21/04/2021)

31:28

Lec 16 || Zero-sum games, Minimax Algorithm (Lec Date: 12/04/2021)

30:09

Lec 15 || Local search techniques, Structure of the problems (Lec Date: 07/04/2021)

26:53

Lec 14 || Backtracking Search for CSPs

29:35

Lec 13 || Constraint Propagation: Inference in CSPs, AC3 algorithm

26:30

Lec 12 || DefiningConstraint Satisfaction Problems (Lec Date: 31/03/2021)

25:35

Lec 11 || Informed Search Strategies & Heuristic Functions (Lec Date: 23/03/2021)

33:43

Lec 10 || Informed Search Strategies (Lec Date: 22/03/2021)

50:58

Lec 8 & 9 || Uninformed Search Strategies (Lec Date: 17/03/2021)

26:22

Lec 7 || Searching for Solutions (Lec Date: 15/03/2021)

41:17

Lec 6 || 3-Missionaries and 3-Cannibals, 8-Queens Puzzle Problems formulation (Lec Date: 10/03/2021)

26:32

Lec 5 || Well-defined problems and problem formulation (Lec Date: 09/03/2021)

28:08

Lec 4 || Types of Agent Programs, Learning Agents (Lec Date: 08/03/2021)

28:11

Lec 3 || Rational Agents, PEAS Descriptors, Types of Environment (Lec Date: 03/03/2021)

20:52

Lec 2 || Foundations, History and Applications of AI || Agent (Lec Date: 02/03/2021)

24:26

Lec 1 || Course Introduction & Definitions of AI (Lec Date: 01/03/2021)

37:23

K-means Clustering numerical examples | 1 (Lec Date: 02/01/2021)

13:03

Decision Tree Numericals | 2 | Gini Impurity

12:24

Lec 29: Applications of DIP | Sonar and Medical Imaging (Lec Date: 24/12/2020)

8:50

Lec 28: Applications of DIP | Satellite and Radar Imaging

35:23

Lec 27: Thresholding based segmentation | Otsu's method (Lec Date: 22/12/2020)

58:55

Decision Tree Numericals | 1 | Information Gain & Entropy (Lec Date: 19/12/2020)

20:46

Lab 11: Shrinking and Zooming an image by interpolation and replication (Deadline: 26/12/2020)

1:22:19

Revision of Data Structure

26:13

Lec 26: Region based Segmentation (Lec Date: 02/12/2020)

28:37

Lec 25: Canny Edge Detector (Lec Date: 01/12/2020)

46:52

Bayes Classifier Numericals | 2

12:32

Lec 24: Edge Detection | LoG operators (Lec Date: 26/11/2020)

20:27

Lec 23: Edge Detection | Gradient Operators (Lec Date: 25/11/2020)