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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