CMI M.Sc. and Ph.D. Computer Science Syllabus 2026

Complete CMI M.Sc. and Ph.D. Computer Science syllabus 2026 with topic-wise breakdown. Download PDF, check weightage, and start your preparation.

📌 Key Takeaways

## Key Takeaways of the CMI Computer Science Syllabus The CMI M.Sc./Ph.D. Computer Science syllabus is not built for memorisation-based preparation. It is built to test whether a student can **reason clearly, write correctly, and solve mathematically grounded computer science problems**. ### What matters most - discrete maths strength - graph comfort - logic precision - automata clarity - algorithmic maturity - mathematical support from probability, calculus, and linear algebra ### What separates top candidates Top candidates are usually not the ones who only solve fast objective questions. They are the ones who can also write **clean descriptive answers** and choose the right questions under pressure.

Complete Syllabus

## CMI M.Sc. and Ph.D. Computer Science Syllabus The official syllabus broadly covers: ### Discrete Mathematics - elementary combinatorics - induction - pigeonhole principle - permutations and combinations - finite set theory - functions and relations ### Logic - boolean logic - truth tables - boolean circuits - and, or, not, nand ### Graphs - basic graph definitions - trees - bipartite graphs - matchings - BFS - DFS - minimum spanning trees - shortest paths ### Formal Languages and Automata Theory - regular expressions - DFA - NFA - subset construction - regular languages - pumping lemma - context free grammars - computability basics ### Algorithms - asymptotic notation - recurrence relations - sorting - searching - complexity analysis ### Algorithmic Techniques - divide and conquer - greedy - dynamic programming ### Data Structures - lists - queues - stacks - binary search trees - heaps ### Probability Theory - finite probability spaces - Bayes theorem - conditional probability - expectation - variance - elementary distributions - inequalities and bounds ### Calculus - limits - continuity - differentiability - functions of several variables - partial derivatives - integral calculus ### Linear Algebra - vector spaces - linear operators - eigenvalues and eigenvectors - determinants - systems of linear equations - linear independence - inner product spaces - symmetric and Hermitian matrices

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