Course Outcomes:
Apply principles of propositional and predicate logic to model, analyze, and validate logical arguments using truth tables, standard forms, rules of inference, and quantifiers.
Use set theory, functions, and relations to represent, manipulate, and reason about mathematical structures and their properties.
Solve combinatorial problems using counting principles, recurrence relations, and graph theory concepts, including paths, connectivity, colouring, and spanning trees.
Implement algorithms for graph and tree problems such as shortest paths, minimal spanning trees, Huffman coding, and topological sorting.
Analyse and apply algebraic structures, including groups, rings, fields, and Boolean algebras, to formulate and solve problems in computer science and related domains.
Unit wise Notes: 1. Unit 1
2. Unit 2
Course Outcomes:
Understand core concepts and types of machine learning.
Apply supervised learning algorithms and handle overfitting.
Implement neural networks and basic deep learning models.
Explore probabilistic models and learning theory.
Use clustering and recommendation techniques for data analysis.
Unit wise Notes: 1. Unit 1
2. Unit 2