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Karnataka PGCET MTech Computer Science Engineering Syllabus 2026 PDF Download

The Karnataka Post Graduate Common Entrance Test (PGCET) for M.Tech in the Computer Stream is the definitive entrance exam for admission into various specialized engineering programs across Karnataka. The 2026 syllabus covers a Common Syllabus applicable to a wide range of courses, including Computer Science & Engineering (CSE), Information Science & Engineering (ISE), Artificial Intelligence and Machine Learning (AI/ML), Data Science, IoT, Cyber Security, and Blockchain Technology.

Success in this exam requires a strong command of core computer science fundamentals, advanced engineering mathematics, and technical English. Below is the comprehensive breakdown of the eleven key sections you must prepare for.

Common Syllabus for PGCET Computer Science Engineering 2026

(Applicable to CSE, ISE, AI, ML, Data Science, IoT, Cyber Security, and related specializations)

1. Engineering Mathematics

This section forms the theoretical backbone of the exam, covering essential mathematical tools for engineering.

  • Linear Algebra: Matrices, determinants, rank, systems of linear equations, Eigen values, and Eigen vectors.
  • Calculus: Limits, continuity, differentiability, partial derivatives, convergence tests, and Fourier series.
  • Vector Calculus: Gradient, divergence, curl, line/surface/volume integrals, and theorems by Stokes, Gauss, and Green.
  • Differential Equations: First-order ODEs (linear/nonlinear), higher-order linear ODEs with constant coefficients, Cauchy’s and Euler’s equations.
  • Partial Differential Equations (PDEs): Formation and solution via direct integration and separation of variables; Heat and Wave equations.
  • Transforms: Laplace, Fourier, and Z-transforms.
  • Probability and Statistics: Mean, median, mode, standard deviation, random variables, distributions (Poisson, Normal, Binomial), correlation, and regression.
  • Numerical Methods: Solving algebraic equations, numerical integration (Trapezoidal/Simpson’s rules), and numerical solutions of ODEs.

2. C Programming for Problem Solving

Candidates must demonstrate proficiency in C syntax and algorithmic implementation.

  • Overview: Program structure, execution, data types, operators, I/O operations, conditional branching, and loops.
  • Applications: Writing programs for quadratic roots, binomial coefficients, and Pascal’s triangle.
  • Arrays & Strings: 1D/2D arrays, character arrays, and string manipulation.
  • Algorithms: Basic searching (linear) and sorting algorithms (bubble sort, selection sort).

3. Technical English

This section evaluates communication skills specific to technical contexts.

  • Phonetics & Listening: Sounds, mispronunciations, silent letters, homophones/homonyms, aspiration, and article usage.
  • Grammar & Usage: Subject-verb agreement, noun-pronoun agreement, tense sequences, misplaced modifiers, prepositions, conjunctions, and gender/number agreement.

4. Data Structures and Algorithms

A critical section focusing on efficient data organization and problem-solving techniques.

  • Abstract Data Types: Stacks, queues, lists, sets, strings, trees (Binary Search Trees), heaps, and graphs.
  • Operations: Traversals, connected components, spanning trees, shortest paths, hashing, sorting, and searching.
  • Design Techniques: Greedy approach, Dynamic Programming, Divide and Conquer.
  • Complexity Analysis: Asymptotic analysis (Best/Worst/Average case), upper/lower bounds, and NP-completeness.

5. Logic Design and Computer Organization

Focuses on the hardware foundation of computing systems.

  • Logic Design: Boolean functions, minimization, combinational and sequential circuit design.
  • Computer Arithmetic: Number representation (fixed/floating point).
  • Organization: Machine instructions, addressing modes, ALU, data path, control units (hardwired/micro-programmed), memory hierarchy, I/O interfaces (Interrupt/DMA), pipelining, and cache/storage systems.

6. Formal Languages and Automata Theory

Covers the theoretical aspects of computation.

  • Topics: Regular languages, Finite Automata, Context-Free Languages, Push Down Automata, Recursively Enumerable sets, Turing Machines, and Undecidability.

7. System Software

Deals with the software that manages computer hardware.

  • Components: Lexical analysis, parsing, syntax, directed translation, runtime environments, code generation, and linking (static/dynamic).

8. Operating Systems

Tests knowledge of system resource management.

  • Core Concepts: Concurrency, synchronization, deadlock, processes, threads, and inter-process communication.
  • Management: CPU scheduling, memory management, file systems, I/O systems, protection, and security.

9. Databases

Focuses on data storage, retrieval, and management.

  • Models: Relational model, ER diagrams, relational algebra, tuple calculus.
  • Design: Integrity constraints, normal forms.
  • Implementation: SQL, file structures (sequential, indexing, B+ trees), transactions, and concurrency control.

10. Computer Networks

Covers communication protocols and network architecture.

  • Architecture: ISO/OSI stack, data encoding/transmission, data link control, sliding window protocols.
  • LAN & Routing: Ethernet, Token Ring, routing protocols, packet switching, switches, and gateways.
  • Protocols: TCP/UDP, Application layer (HTTP, SMTP, DNS, FTP), and network security.

11. Web Technologies

Evaluates understanding of modern web development frameworks.

  • Architecture: Three-tier web-based architectures.
  • Technologies: JSP, ASP, J2EE, .NET systems, HTML, and XML.
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