Curriculum & Course Catalogue
CSENEP-2020 Restructured SchemeAuthoritative curriculum database of core theory courses, discipline & stream electives, practical computing laboratories, and capstone research modules offered across Undergraduate (UG), Postgraduate (PG), and Doctoral (Ph.D.) levels by the Department of Computer Science & Engineering.
54
Across All Programs34
B.Tech & Dual Degree12
M.Tech AI & Computing8
Advanced Research Modules10
Software & Systems LabsShowing 54 courses
Academic Session 2024-2025Introduction to Computer and C Programming
Fundamentals of computing, algorithm development, flowcharting, C syntax, data types, control flow, functions, recursion, arrays, pointers, dynamic memory allocation, file I/O, and structured problem solving in laboratory sessions.
Discrete Structures
Propositional and predicate calculus, proof techniques, sets, relations, functions, algebraic structures, groups, monoids, lattices, Boolean algebras, graph theory, trees, Eulerian and Hamiltonian graphs, and recurrence relations.
Object Oriented Programming
Object-oriented software design paradigms, data encapsulation, inheritance, polymorphism, abstract classes, interface design, exception handling, templates, Standard Template Library (STL) containers, algorithms, and modern C++ programming.
Data Structures
Linear and non-linear data structures, asymptotic notation, stacks, queues, circular buffers, singly/doubly linked lists, binary trees, binary search trees, AVL trees, heaps, priority queues, hashing techniques, collision resolution, and graph representations.
Computer Graphics
Computer graphics hardware, raster algorithms, Bresenham line and circle algorithms, 2D and 3D affine transformations, viewing transformations, window-to-viewport mapping, Cohen-Sutherland clipping, 3D projections, hidden surface elimination, shading models, and OpenGL.
Object Oriented Programming Lab
Practical implementation of object-oriented programming principles in C++/Java. Laboratory exercises covering operator overloading, virtual functions, templates, STL, file streams, and mini-project design.
Data Structures Lab
Hands-on implementation of abstract data types, linked list variations, expression parsing using stacks, queue simulations, tree traversals, balance rotations in AVL trees, heap operations, and graph search algorithms (BFS/DFS).
Computational Tools and Workshop
Practical mastery of Linux operating environment, shell programming (Bash), text processing with awk/sed, Git version control, LaTeX technical document preparation, Python automation scripts, and development toolchains.
Theoretical Computer Science (Minor Degree)
Formal language theory, regular expressions, deterministic and non-deterministic finite automata, pumping lemma, context-free grammars, pushdown automata, Turing machines, decidability, halting problem, and Chomsky hierarchy.
Data Structures (Open Elective)
Open elective on data structures for non-CSE students: arrays, lists, stacks, queues, binary trees, sorting algorithms, searching methods, basic graph algorithms, and real-world application case studies.
Analysis & Design of Algorithms
Algorithm design strategies, divide-and-conquer, greedy algorithms, dynamic programming, branch-and-bound, amortized analysis, string matching (KMP, Rabin-Karp), network flows, NP-completeness, reduction techniques, and approximation algorithms.
Compiler Design
Structure of a compiler, lexical analysis (Lex/Flex), syntax analysis, top-down and bottom-up parsing (LL, LR, LALR), syntax-directed translation, intermediate code generation (three-address code), type checking, code optimization, and target code generation.
Computer Networks
Computer networking architectures, OSI and TCP/IP protocol stacks, framing, error control, medium access control (CSMA/CD, CSMA/CA), IPv4/IPv6 addressing, subnetting, routing algorithms (OSPF, BGP), transport protocols (TCP flow and congestion control, UDP), and application protocols.
Artificial Intelligence
Foundations of artificial intelligence, state space search, heuristic search (A*, IDA*), adversarial search, minimax, alpha-beta pruning, knowledge representation, first-order logic, resolution refutation, probabilistic reasoning, Bayesian networks, and machine learning fundamentals.
Discipline Elective - II (Advanced Operating System)
Advanced operating systems concepts, multi-core scheduling, distributed operating systems, synchronization mechanisms, distributed shared memory, virtualization hypervisors, real-time operating systems (RTOS), and Linux kernel internals.
Discipline Elective - III (Graph Theory)
Graph-theoretic fundamentals, connectivity, matching theory, bipartite graphs, vertex and edge colorings, planar graphs, Kuratowski theorem, Ramsey theory, spectral graph theory, random graphs, and algorithmic graph problems.
Compiler Design Lab
Compiler design laboratory implementing lexical analyzers using Flex, grammar specification and parser generation using Bison/Yacc, syntax tree building, intermediate code emitters, and basic optimizations.
Computer Networks Lab
Computer networks laboratory involving network packet sniffing with Wireshark, socket programming in C/Python, TCP/UDP client-server implementations, simulation of routing protocols using NS-3/Cisco Packet Tracer, and network configuration.
Advance Computer Architecture
Instruction level parallelism, pipelining hazards, dynamic branch prediction, speculative execution, superscalar processors, memory hierarchy design, cache coherence protocols (MESI, MOESI), snooping vs. directory-based systems, and GPU architectures.
Information Security & Privacy
Security principles, CIA triad, classical ciphers, symmetric cryptography (AES), public-key cryptography (RSA, ECC), cryptographic hash functions, digital signatures, authentication protocols, network security (IPsec, TLS), privacy preservation, and differential privacy.
Data Warehousing & Data Mining
Data warehouse architectures, OLAP cubes, multidimensional modeling, ETL processes, data mining methodologies, association rule mining (Apriori, FP-growth), classification algorithms (Decision Trees, Naive Bayes, SVM), clustering (K-means, DBSCAN), and outlier detection.
DE-V Information Theory and Coding
Information theory foundations, entropy, mutual information, channel capacity, Shannon source coding theorem, Huffman coding, arithmetic coding, channel coding theorem, linear block codes, cyclic codes, and convolutional codes.
DE-V Big Data Analytics
Big data ecosystems, Hadoop distributed file system (HDFS), MapReduce programming paradigm, Apache Spark architecture, Resilient Distributed Datasets (RDDs), Spark SQL, stream processing with Kafka, and NoSQL databases.
SC II Advance Mobile Communication
Wireless propagation models, path loss, fading, cellular architecture, frequency reuse, handoff strategies, 4G LTE architectures, 5G NR numerology, beamforming, massive MIMO, network slicing, and mobile edge computing.
SC II Deep Learning
Deep learning architectures, backpropagation mathematics, optimization algorithms (Adam, RMSProp), convolutional neural networks (CNNs), residual networks, recurrent neural networks (RNNs, LSTMs, GRUs), attention mechanisms, and Transformer networks.
SC III Internet of Things
IoT architecture layers, sensing and actuation, embedded hardware (Raspberry Pi, ESP32), IoT communication protocols (MQTT, CoAP, BLE, LoRaWAN), cloud integration, edge analytics, and industrial IoT case studies.
SC III Pattern Recognition
Statistical pattern recognition, Bayes decision theory, parameter estimation, maximum likelihood, Parzen windows, linear discriminant analysis (LDA), support vector machines, clustering, feature extraction, and dimensionality reduction.
Information Security & Privacy Lab
Information security laboratory covering vulnerability scanning, cryptographic protocol implementation, symmetric and asymmetric key generation, penetration testing basics, Wireshark security analysis, and firewall configuration.
Data Warehousing & Data Mining Lab
Data warehousing and data mining laboratory implementing ETL pipelines, data preprocessing, association rule mining, decision tree classifiers, clustering algorithms in Python/R, and visualization dashboards.
Advance Topics in Software Engineering
Advanced software engineering paradigms / topics in networks: agile frameworks, microservices architecture, formal verification, continuous integration/continuous deployment (CI/CD), software metrics, software reliability, and empirical software engineering.
Applied Optimization
Formulation of optimization problems, unconstrained optimization, gradient descent, Newton methods, constrained optimization, KKT conditions, linear programming, simplex algorithm, duality, convex optimization, and heuristic optimization techniques.
Deep Learning for Computer Vision
Computer vision pipelines, image representation, spatial filtering, edge detection, feature descriptors (SIFT, ORB), object detection frameworks (YOLO, Faster R-CNN), semantic segmentation (U-Net, Mask R-CNN), and vision transformers.
Architecture of Large Systems
Design of large-scale distributed systems, architectural patterns, high availability, fault tolerance, consensus protocols (Paxos, Raft), CAP theorem, distributed databases, event-driven architectures, and scalable microservices.
Data Ware Housing & Data Mining (Lab)
Advanced data warehousing and data mining laboratory focusing on large-scale dataset analytics, feature engineering, predictive modeling, and deep data exploration.
Topics in Computer Networks
Advanced software engineering paradigms / topics in networks: agile frameworks, microservices architecture, formal verification, continuous integration/continuous deployment (CI/CD), software metrics, software reliability, and empirical software engineering.
Artificial Intelligence and Intelligent Systems
Advanced artificial intelligence, intelligent agent architectures, automated planning, constraint satisfaction problems, probabilistic reasoning over time, hidden Markov models, Markov decision processes, and reinforcement learning.
Mathematics for Machine Learning
Linear algebra foundations for machine learning, vector spaces, eigenvalues/eigenvectors, singular value decomposition (SVD), multivariate calculus, gradients, Jacobians, Hessians, probability distributions, continuous random variables, and estimation theory.
Applied Optimization
Formulation of optimization problems, unconstrained optimization, gradient descent, Newton methods, constrained optimization, KKT conditions, linear programming, simplex algorithm, duality, convex optimization, and heuristic optimization techniques.
AI based Programming Lab
Hands-on programming laboratory in artificial intelligence and machine learning: Python tensor computations with PyTorch/TensorFlow, implementing heuristic search, supervised/unsupervised algorithms, and deploying AI models.
PE-I (Generative AI)
Foundations of generative artificial intelligence: autoregressive models, variational autoencoders (VAEs), generative adversarial networks (GANs), score-based diffusion models, large language model (LLM) fine-tuning (LoRA), and multimodal generative systems.
PE-II (Data Structures and Algorithms)
Advanced algorithmic design and data structures for postgraduates: randomized algorithms, self-adjusting data structures (splay trees, skip lists), disjoint sets, advanced amortized analysis, persistent data structures, and approximation algorithms.
Theoretical Computer Science
Advanced theoretical computer science: computational complexity classes, space complexity, Savitch theorem, PSPACE, polynomial-time hierarchy, probabilistic complexity classes (BPP, RP), interactive proof systems, and circuit complexity.
Computer Systems
Advanced computer systems design: processor microarchitecture, memory consistency models, cache hierarchies, virtualization architectures, high-performance I/O subsystems, operating system abstractions, and parallel computing architectures.
Computational Lab-I
Postgraduate computational laboratory I: advanced systems programming, multi-threaded server implementation, distributed message passing (MPI), GPU programming with CUDA, and performance profiling.
PE-I (Advance Computer Networks)
Advanced computer networks: next-generation internet architecture, software-defined networking (SDN), OpenFlow protocol, network function virtualization (NFV), datacenter networking, congestion control algorithms (BBR), and 5G core network architecture.
PE-II (Digital Image Processing)
Design of large-scale distributed systems, architectural patterns, high availability, fault tolerance, consensus protocols (Paxos, Raft), CAP theorem, distributed databases, event-driven architectures, and scalable microservices.
Research Methodologies in Computing
Doctoral research methodologies in computer science: literature survey methodologies, formulating research questions, scientific hypothesis testing, benchmark dataset design, experimental design, and doctoral research thesis formulation.
State-of-the-Art Seminar & Research Formulation
State-of-the-art research seminar: critical evaluation of recent ACM/IEEE Transactions publications, seminar presentation, technical discourse, formulation of dissertation proposals, and research peer review.
Advanced Directed Study in Computing Systems
Independent directed study in advanced computer systems, autonomous research investigation under faculty advisory, survey paper preparation, and system prototype implementation.
Research and Publication Ethics (RPE)
UGC mandated Research and Publication Ethics (RPE): philosophy of science, research integrity, publication misconduct, plagiarism detection tools (Turnitin, Urkund), predatory journals, and open access publishing.
Mathematical Foundations for Computer Science Research
Mathematical foundations for computer science research: discrete probability, spectral graph theory, randomized algorithms, convex optimization, matrix decompositions (SVD, PCA), and concentration inequalities.
Advanced Topics in AI & Machine Learning
Advanced research topics in artificial intelligence and machine learning: frontier deep learning, self-supervised learning, generative AI, diffusion models, reinforcement learning with human feedback (RLHF), and explainable AI (XAI).
Frontier Research in Wireless & Distributed Networks
Frontier research in wireless and distributed networks: software-defined 5G/6G networks, vehicular networks (VANET), Internet of Things protocols, edge intelligence, and distributed consensus mechanisms.
Advanced Computational Complexity & Algorithm Theory
Advanced computational complexity and algorithm theory: complexity classes (P, NP, PSPACE, BPP, IP, PCP theorem), hardness of approximation, parameterized complexity, circuit complexity, and quantum complexity foundations.