B.Sc. (Computer Science) Artificial Intelligence Syllabus - Mumbai University
This is the TY BSc Computer Science syllabus under NEP 2020, in force from the academic year 2026-27. The University still sets the earlier Choice Based papers alongside it — her Summer 2026 third-year timetables name that scheme — so check which scheme your exam form names before you revise.
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Syllabus for Artificial Intelligence
The University sets the practical for this subject separately, in Computer Science Practical 5, as Module 1. It carries its own credits, so it is examined as a paper of its own.
Module I
- Foundations of AI & Intelligent Agents: What is AI? Rational agents vs human thinking, Computational agents, Agent–Environment interaction, Types of environments, Agent architectures (simple reflex, model-based, goal-based, utility- based, learning agents)
- Problem Solving & Search: Problem formulation, Uninformed search: BFS, DFS, Uniform Cost, IDS, Informed search: Greedy, A*, Heuristics: admissibility & consistency, Adversarial search: Minimax & Alpha-Beta pruning
- Knowledge Representation & Logical Reasoning: Knowledge-based agents, Propositional logic & inference, First-Order Logic, Rule-based systems, Planning basics (STRIPS concept), Fuzzy Logic & Fuzzification
- Reasoning Under Uncertainty: Probabilistic reasoning, Bayes theorem, Conditional independence, Bayesian Networks
Module II
- Introduction of Machine Learning: Forms of learning (supervised, unsupervised, reinforcement), Parametric vs Nonparametric models, Bias–variance tradeoff, Overfitting & regularization, Gradient descent (intuitive)
- Supervised Learning Models: Classification vs Regression, k-NN, Decision Trees, Naive Bayes, SVM, Artificial Neural Networks:(single-layer & concept of deep learning, Ensemble methods & Boosting
- Probabilistic & Latent Variable Models: Statistical learning framework, Maximum Likelihood Estimation, Learning with complete data, Hidden variables, EM Algorithm, Hidden Markov Models
- Unsupervised & Reinforcement Learning: Concept of clustering, Association rule mining (Apriori concept), Reinforcement learning framework, Markov Decision Processes, Q-Learning (update rule intuition)
- Responsible AI: Ethical issues in AI systems, Bias and fairness in AI models, Transparency and explainability, Accountability and human oversight, Risks in Generative AI (hallucination, deepfakes, misuse)
Text Books
- 1 Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, 3rd Edition, Pearson, 2010.
- 1 Artificial Intelligence: Foundations of Computational Agents, David L Poole,Alan K. Mackworth, 2nd Edition, Cambridge University Press ,2017.
- 2 Artificial Intelligence, Kevin Knight and Elaine Rich, 3rd Edition, 2017 3) The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, 2013
Reproduced from the University of Mumbai syllabus for B.Sc. (Computer Science) under NEP 2020, in force from the academic year 2026-27. Wording is as printed in that syllabus. Module numbering is as printed there too.