Syllabus Mathematical Foundation for AI - (BC03001041) Total Credits L+T+ (PR/2) Assessment Pattern and Marks Total Marks C Theory Tutorial / Practical ESE (E) PA / CA (M) PA / CA (I) ESE (V) 4 70 30 20 30 150 Unit No. Content 1 Linear Algebra for AI : Vectors, matrices, operations, eigenvalues, eigenvectors, SVD. (Chapter - 1) 2 Probability and Statistics : Probability theory, Bayes theorem, distributions, expectation, variance, law of large numbers. (Chapter - 2) 3 Calculus and Optimization : Limits, derivatives, partial derivatives, gradient descent, convex functions, Lagrange multipliers. (Chapter - 3) 4 Discrete Mathematics and Logic : Sets, relations, functions, propositional and predicate logic, inference. (Chapter - 4) 5 Applications and Case Studies : Mathematical modeling in AI, optimization in neural networks, probabilistic reasoning in ML. (Chapter - 5)
