CSE-241(2025)

Theory: 70%; Laboratory: 30%.

Theory(70%): Quiz:14%. Mid-semester: 36%; Ensemester: 50%. 

Lecture 1(31.12.2024): Introduction to Artificial Intelligence (AI)

Lecture 2(01.01.2025): Multidimentional Definition of AI and Turing Test

Lecture 3 (06.01.2025):  AI and Machine Learning : Supervised Learing Lecture Slides 

Lecture 4(07.01.2025): Definition of Classifier and Decision Tree Induction Learing Lecture Slides    

Lecture 5(08.01.2025): Various Measure to compute Impurity of Nodes of Decision Tree Lecture Slides       

Lecture 6(13.01.2025):  Model Overfitting and Cause of Model Overfitting , Addressing Model Overfitting by Estimating Generalization Error Lecture Slides 

Lecture 7(14.01.2025):  Bayes Classifiers Lecture Slides   

Lecture 8 (15.01.2025):  Distance based Classifiers: KNN, Weighted KNN Lecture Slides 

Lecture 9 (20.01.2025): Introduction Support Vector Machine Classifier Scribed                   Lecture Video of Prof Patrick Winston, MIT [ Suggested to watch 0-38.34 th Minutes]  

Lecture 10 (21.01.2025) : Support Vector Machine Classifier and Convex Optimization Lecture Video  Lecture Notes

Lecture 11 (22.01.2025) : Support Vector Machine Classifier Lecture Slides   Scribed

(Online Quadratic Optimization Solver) 

Lecture 12(27.01.2025): Numerical Examples : Linear Support Vector Machine Scribed Lecture

Lecture 13(28.01.2025):  Linear SVM with Non Separable Decision Hyperplane and Non Linear Support Vector Machine Lecture Slide 

Lecture 14(29.01.2025): Introduction to Unsupervised Learning: K-means Clustering  Lecture Slides

Lecture 15(04.02.2025): Higherarchical Clustering. Lecture Slides

Lecture 16(05.02.2026): Density based Clustering: DBSCAN Lecture Slides

MID-TERM EXAMINATION: 12.02.2025 

Lecture  17(18.02.2025):  Intelligent Agent and Concept of Rationality Lecture Slides

Lecture 18(19.02.2025): Problem Solving Agent and Uninformed Search Lecture Slides

Lecture 19(24.02.2025): Uninformed Search  Lecture Slides

Lecture 20(25.02.2025): Informed Search: A* Search Lecture Slides

Lecture 21(03.03.2025): A* Search:  Few Examples Lecture Slides

Lecture 22(04.03.2025):  A* Search:  Few Examples

Lecture 23 (05.03.2025):   Local Search Algorithms: Hill-climbing search Lecture Slides

Lecture 24 (17.03.2025): AO* and Min-Max Algorithm Lecture Slides

Lecture 25 (19.03.2025): AO* and Min-Max Algorithm Lecture Slides 

Lecture 26 (24.03.2025):  Density Based Clustering, Cluster Validity  Evaluation Metrics of Clustering Techniques I 

Lecture 27 (25.03.2025): Ensemble of Classifier: Bagging 

Lecture 29 (26.03.2025): Ensemble of Classifier: Boosting 

Lecture 30 (07.042025): Artificial  Neural Network I 

Lecture 31 (08.04.2025): Artificial  Neural Network II 

Lecture 32 (09.04.2025): 

Lecture 33 (15.04.2025):

Lecture 34 (16.04.2025):