Syllabus (COMPUTER APPLICATION)

Course Type: MAJ-13

Semester: 7

Course Code: BBCAMAJ13C

Course Title: Artificial Intelligence and Machine Learning

(L-P-Tu): 4-2-0

Credit: 6

Practical/Theory: Combined

Course Objective: Course Objectives: Recognize and articulate the significance of AI in addressing real-world problems. Explore and acquire knowledge of search techniques in AI. Gain understanding and proficiency in knowledge representation techniques in AI. Comprehend the basic concepts and principles of machine learning. Study and analyze neural networks and clustering techniques in AI applications.

Learning Outcome: Course Outcomes: After the completion of this course, students will be able to: Apply search techniques effectively, evaluate and compare solutions. Analyze and evaluate knowledge representation techniques in AI systems. Demonstrate an understanding of probabilistic reasoning and apply it to practical scenarios. Implement machine learning solutions for classification, regression, and clustering problems. Design and implement machine learning algorithms in real-world applications, understanding their capabilities and limitations.

Syllabus:

Unit I: Theory Credit: 4 (L 60)

Introduction to AI: Overview of Artificial Intelligence, Examples of AI Systems, AI Technique, Explaining AI through Tic-Tac-Toe Problem. [L 5]

Search Techniques: Conventional and Heuristic Search Strategies, Hill Climbing Search, Simulated Annealing Search, Greedy Best-First Search, A* Search, Constraint Satisfaction Problems, Mini-Max Search Procedure, Alpha-Beta Pruning. [L 8]

Knowledge, Reasoning, and Planning: Knowledge Representation and Mapping, Predicate Logic, Forward and Backward Reasoning, Matching, Representing Knowledge in an Uncertain Domain, Bayesian Networks, Components of a Planning System, Goal Stack Planning, Hierarchical Planning. [L 8]

Introduction to Machine Learning: Machine Learning, Types of Machine Learning, Applications, Learning, Iterations, Epoch, Batch. [L 8]

Supervised Learning: Basics of Feature Selection and Evaluation, Noisy Data, Bias-variance Trade-off, Underfitting and Overfitting, Classification and Regression, Logistic Regression, Decision Trees, Pruning in Decision Trees, Support Vector Machines and Kernels. [L 10]

Neural Networks: Perceptrons, Representational Limitation of Perceptrons, Gradient Descent Training, Multilayer Networks, Backpropagation, Exploding, and Vanishing Gradients. [L 8]

Unsupervised and Semi-supervised Learning: Learning from Unclassified Data, Clustering, Partitioning Clustering (K-Means, K- Medoid), Hierarchical Clustering (Agglomerative and Divisive), Association Rule Mining. [L 8]

Ensemble: Committees of Multiple Hypotheses, Bagging, Boosting, Active Learning with Ensembles. [L 5]

Unit II: AI and ML Lab using Python Credit: 2 (L 60)

Practical part will be based upon the modules covered in the theory part.

Reading References:

1. S. Russel, P. Norvig, Artificial Intelligence a Modern Approach, 3rd edition, Pearson Education.
2. E. Rich, K. Knight, Artificial Intelligence, 3rd edition, TMH, New Delhi.
3. Tom Mitchell, Machine Learning, Latest Edition, Mc-Graw Hill.
4. Shai Shalev-Shwartz, Shai Ben-David, Understanding Machine Learning, Cambridge University Press, 2017.