Comprehensive Course Catalog

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Pre-requisites
I	CS101	Introduction to Programming	3-0-0-3	-
I	CS102	Mathematics for Computer Science	4-0-0-4	-
I	CS103	Computer Organization and Architecture	3-0-0-3	-
I	CS104	Engineering Graphics	2-0-0-2	-
I	CS105	Communication Skills	2-0-0-2	-
I	CS106	Introduction to Algorithms	3-0-0-3	-
II	CS201	Data Structures and Algorithms	4-0-0-4	CS101
II	CS202	Database Management Systems	3-0-0-3	CS101
II	CS203	Operating Systems	3-0-0-3	CS103
II	CS204	Discrete Mathematics	4-0-0-4	CS102
II	CS205	Object-Oriented Programming with Java	3-0-0-3	CS101
III	CS301	Computer Networks	3-0-0-3	CS203
III	CS302	Software Engineering	3-0-0-3	CS201
III	CS303	Compiler Design	3-0-0-3	CS201
III	CS304	Artificial Intelligence	3-0-0-3	CS201
III	CS305	Computer Graphics and Visualization	3-0-0-3	CS201
IV	CS401	Distributed Systems	3-0-0-3	CS301
IV	CS402	Machine Learning	3-0-0-3	CS304
IV	CS403	Cybersecurity	3-0-0-3	CS203
IV	CS404	Data Mining and Analytics	3-0-0-3	CS302
V	CS501	Big Data Technologies	3-0-0-3	CS404
V	CS502	Embedded Systems	3-0-0-3	CS203
V	CS503	Web Development	3-0-0-3	CS201
V	CS504	Mobile Application Development	3-0-0-3	CS205
V	CS505	User Experience Design	3-0-0-3	CS201
VI	CS601	Cloud Computing	3-0-0-3	CS401
VI	CS602	Internet of Things	3-0-0-3	CS502
VI	CS603	Quantitative Finance	3-0-0-3	CS404
VI	CS604	Reinforcement Learning	3-0-0-3	CS402
VII	CS701	Capstone Project I	3-0-0-3	CS501
VII	CS702	Capstone Project II	3-0-0-3	CS701
VIII	CS801	Research Thesis	4-0-0-4	CS702

Advanced Departmental Electives

Deep Learning and Neural Networks: This course explores advanced architectures like CNNs, RNNs, LSTMs, Transformers, and GANs. Students gain hands-on experience with frameworks like TensorFlow and PyTorch while working on real-world datasets.

Reinforcement Learning: Focused on decision-making algorithms in uncertain environments, this course covers Markov Decision Processes, Q-Learning, Policy Gradients, and Actor-Critic methods. Students implement agents that learn optimal behaviors through interaction with simulated environments.

Blockchain Technology and Smart Contracts: This elective introduces students to distributed ledger technologies, consensus mechanisms, cryptographic protocols, and smart contract development using Ethereum and Hyperledger Fabric. Practical labs involve building decentralized applications (dApps).

Human-Centered Design for AI Systems: Combining principles of UX design with machine learning models, this course emphasizes ethical considerations in AI deployment, user privacy protection, and inclusive system design practices.

Quantum Computing Fundamentals: Students learn about quantum bits (qubits), entanglement, superposition, and quantum algorithms. Labs include simulation of quantum circuits using Qiskit and IBM Quantum Experience platforms.

Computer Vision and Image Processing: Covers image enhancement, segmentation, feature extraction, object detection, and recognition techniques using convolutional neural networks (CNNs). Projects involve analyzing medical images or autonomous vehicle sensor data.

Natural Language Processing: Explores text classification, sentiment analysis, language modeling, and translation models. Students build chatbots, summarizers, and question-answering systems using transformer-based architectures like BERT and GPT.

Edge AI and IoT Systems: Focuses on deploying machine learning models on resource-constrained devices such as microcontrollers and embedded platforms. Emphasis is placed on model compression techniques, energy efficiency, and real-time inference.

Big Data Engineering with Spark: Introduces Apache Spark for processing large-scale datasets efficiently. Labs involve writing MapReduce jobs, optimizing data pipelines, and implementing streaming analytics using Kafka and Storm.

Cybersecurity and Ethical Hacking: Covers network security protocols, cryptographic systems, penetration testing methodologies, and vulnerability assessment tools. Students simulate attacks on networks to understand defensive strategies.

Software Architecture and Design Patterns: Examines architectural patterns such as microservices, event-driven architectures, and cloud-native solutions. Students design scalable software systems using UML diagrams and domain-driven design principles.

Project-Based Learning Philosophy

The department's philosophy on project-based learning is centered around experiential education that bridges theory with practice. Mini-projects are assigned from the second semester onwards, allowing students to apply learned concepts in controlled settings. These projects often mirror real-world challenges and encourage interdisciplinary thinking.

Mini-Projects

Mini-projects typically span 6–8 weeks and involve teams of 3–5 students. Each project is guided by a faculty member and evaluated based on technical execution, creativity, presentation quality, and peer collaboration. Projects may include developing mobile apps, implementing data visualization dashboards, or designing simple AI agents.

Final-Year Thesis/Capstone Project

The capstone project is the culmination of a student's academic journey, requiring them to tackle an industry-relevant problem using advanced techniques. Students are paired with faculty mentors based on their interests and strengths. The process includes proposal development, literature review, implementation, testing, documentation, and public defense.

Project selection involves a formal application process where students submit proposals outlining objectives, methodology, timeline, and expected outcomes. Faculty members provide feedback during the proposal stage to refine ideas and ensure feasibility.