Syllabus Explorer
Mumbai University · B.E. Artificial Intelligence & Data Science · Semester III (NEP-2020). Every unit links to its subject page, papers and question sets.
Mathematics for Computer Engineering (N2113111)
60 marks · 2 hrs · Exam 17 Nov 2026
| Unit | Title & topics | Resources |
|---|---|---|
| Unit 1 | Linear Algebra Eigenvalues & eigenvectors, Diagonalisation, Cayley–Hamilton theorem | Subject → |
| Unit 2 | LPP & NLPP Lagrange multipliers (NLPP), Solving an LPP (Simplex method), LPP standard form & dual | Subject → |
| Unit 3 | Modular Arithmetic Chinese Remainder Theorem (CRT), RSA algorithm, Remainder by modular arithmetic | Subject → |
| Unit 4 | Fourier Series Full Fourier expansion, Finding an in a Fourier expansion | Subject → |
| Unit 5 | Statistics Spearman rank correlation, Karl Pearson correlation coefficient, Regression lines, Fitting a straight line y = a + bx | Subject → |
| Unit 6 | Probability Moments (raw moments, moments about origin / mean), Moment generating function (MGF), Poisson distribution, Normal distribution | Subject → |
Discrete Structures & Graph Theory (N2113112)
60 marks · 3 hrs · Exam 19 Nov 2026
| Unit | Title & topics | Resources |
|---|---|---|
| Unit 1 | Crisp Set Theory and Logic Mathematical induction, Logical equivalence, Truth table / access expression | Subject → |
| Unit 2 | Mathematical Relations Transitive closure / Warshall, Hasse diagram / lattice, Equivalence relation | Subject → |
| Unit 3 | Functions Function composition, Mapping: function / one-one / onto / many-one | Subject → |
| Unit 4 | Counting Inclusion–exclusion, Recurrence relation, Pigeonhole principle | Subject → |
| Unit 5 | Algebraic Structures Group under × mod 7, Ring | Subject → |
| Unit 6 | Graph Theory Graph isomorphism, Euler / Hamiltonian paths & circuits | Subject → |
Analysis of Algorithms (N2113113)
60 marks · 2 hrs · Exam 21 Nov 2026
| Unit | Title & topics | Resources |
|---|---|---|
| Unit 1 | Introduction Recurrence solving, Selection Sort, Insertion Sort, Asymptotic notations, Big Theta (Θ) | Subject → |
| Unit 2 | Divide and Conquer Approach Merge Sort (FIXED 10 MARKS), Binary Search, Divide and Conquer | Subject → |
| Unit 3 | Greedy Method Approach Kruskal's MCST, Dijkstra's shortest path, Fractional Knapsack, Prim's MST, Greedy general method | Subject → |
| Unit 4 | Dynamic Programming Approach Multistage Graph, LCS — Longest Common Subsequence, 0/1 Knapsack by DP, Floyd-Warshall, Principle of Optimality | Subject → |
| Unit 5 | Backtracking and Branch and Bound Backtracking family, Graph Coloring, N-Queen Problem, 15-puzzle, Sum of Subset | Subject → |
| Unit 6 | String Matching Algorithms KMP String Matching, Rabin-Karp, Naïve String Matching | Subject → |
Computer Organization & Architecture (N2113114)
60 marks · 2 hrs · Exam 25 Nov 2026
| Unit | Title & topics | Resources |
|---|---|---|
| Unit 1 | Computer Fundamentals Number conversion | Subject → |
| Unit 2 | ALU Operations Non-restoring division, Booth's algorithm, IEEE 754 floating point | Subject → |
| Unit 3 | Processor Organization and Control Unit Design 8086 architecture, Microprogrammed control unit, Hardwired vs Softwired CU, Horizontal vs Vertical microinstruction, Addressing modes of 8086 | Subject → |
| Unit 4 | Memory Systems Organization Cache mapping, Memory hierarchy / characteristics, Associative memory | Subject → |
| Unit 5 | I/O Organization I/O methods, ISA / PCI / USB buses, Bus arbitration | Subject → |
| Unit 6 | Parallel Processing Pipeline hazards, Flynn's classification, 80386DX operation modes, Instruction pipeline | Subject → |
Generative AI (Open elective)
60 marks · 2 hrs · Exam 27 Nov 2026
| Unit | Title & topics | Resources |
|---|---|---|
| Unit 1 | Introduction to Generative AI, Language Models & LLM Architectures RNN — architecture & working, LLM — Large Language Models, Generative AI — definition & scope, Challenges in Generative AI, Importance in five real-world domains, Traditional vs modern language models, Transformer architecture, LSTM, Tokenization in transformers | Subject → |
| Unit 2 | GPT, ChatGPT, Ethics & Generative AI Tools GPT architecture & working, GPT variants, Ethics in Generative AI, Generative AI tools / applications, Pre-training, Chat-GPT, Multi-head mechanism in GPT, Self-attention in GPT, GPT and Chat-GPT — definition | Subject → |
| Unit 3 | Asked anyway — not in the syllabus CNN — architecture & working, Importance of data in training | Subject → |
Syllabus structure follows the university's NEP-2020 scheme for this program. Official syllabus PDFs will be linked here when available from the university.