M.Tech CSEMaster of Technology — Computer Science

Advanced algorithm design and computational complexity.

Distributed databases, NoSQL and data management.

Advanced ML techniques — ensemble, deep and reinforcement learning.

Research methods, paper writing and academic integrity.

Deep neural architectures for vision tasks.

Advanced cloud architecture and distributed systems.

NLP, transformers and large language model applications.

Quantum bits, gates, algorithms and quantum machine learning.

Advanced security, privacy-preserving techniques.

Research proposal and initial thesis work.

State-of-the-art vision models — YOLO, ViT and multi-modal.

Enterprise architecture patterns, DDD and event-driven design.

Final thesis, results and defence.

Writing and publishing research papers, patents and IP management.

Industry placement or sponsored research project.

Deep dive into ensemble methods, kernel machines and probabilistic models.

Parallel algorithms, OpenMP, MPI and GPU programming with CUDA.

Probabilistic analysis, randomised algorithms and complexity theory.

Advanced algorithm design, amortised analysis and NP-hardness.

SDN, NFV, 5G networks and advanced routing protocols.

Formal specification, model checking and theorem proving for critical systems.

Query optimisation, transaction management and distributed databases.

XAI techniques, model interpretability, fairness and responsible deployment.

GANs, VAEs, diffusion models and large language model architectures.

Advanced attack vectors, forensic investigation and threat intelligence.

Edge AI deployment, TinyML and distributed intelligence at the edge.

Distributed training, model serving and MLOps at production scale.

Research design, literature review, paper writing and citation ethics.

Major research project contributing original knowledge to CS.

Tech startup ecosystem, IP protection and commercialisation of research.