Course Structure Across All Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | CS101 | Engineering Mathematics I | 3-1-0-4 | - |
| I | CS102 | Physics for Computer Applications | 3-1-0-4 | - |
| I | CS103 | Chemistry for Computer Applications | 3-1-0-4 | - |
| I | CS104 | Introduction to Programming using C | 2-0-2-3 | - |
| I | CS105 | Problem Solving and Programming Lab | 0-0-4-2 | - |
| I | CS106 | English for Communication | 2-0-0-2 | - |
| II | CS201 | Engineering Mathematics II | 3-1-0-4 | CS101 |
| II | CS202 | Data Structures and Algorithms | 3-1-0-4 | CS104 |
| II | CS203 | Database Management Systems | 3-1-0-4 | CS202 |
| II | CS204 | Object Oriented Programming with Java | 2-0-2-3 | CS104 |
| II | CS205 | Java Lab | 0-0-4-2 | CS204 |
| III | CS301 | Computer Organization and Architecture | 3-1-0-4 | CS202 |
| III | CS302 | Operating Systems | 3-1-0-4 | CS202 |
| III | CS303 | Software Engineering | 3-1-0-4 | CS202 |
| III | CS304 | Web Technologies | 3-1-0-4 | CS204 |
| III | CS305 | Web Development Lab | 0-0-4-2 | CS304 |
| IV | CS401 | Computer Networks | 3-1-0-4 | CS301 |
| IV | CS402 | Artificial Intelligence | 3-1-0-4 | CS302 |
| IV | CS403 | Cybersecurity Fundamentals | 3-1-0-4 | CS302 |
| IV | CS404 | Cloud Computing | 3-1-0-4 | CS401 |
| IV | CS405 | Mini Project I | 0-0-6-2 | - |
| V | CS501 | Data Science and Analytics | 3-1-0-4 | CS302 |
| V | CS502 | Mobile Application Development | 3-1-0-4 | CS404 |
| V | CS503 | Human-Computer Interaction | 3-1-0-4 | CS402 |
| V | CS504 | Embedded Systems | 3-1-0-4 | CS301 |
| V | CS505 | Mini Project II | 0-0-6-2 | - |
| VI | CS601 | Advanced Machine Learning | 3-1-0-4 | CS501 |
| VI | CS602 | Big Data Analytics | 3-1-0-4 | CS501 |
| VI | CS603 | IoT and Edge Computing | 3-1-0-4 | CS504 |
| VI | CS604 | Capstone Project | 0-0-8-4 | - |
| VI | CS605 | Research Methodology | 2-0-0-2 | - |
| VII | CS701 | Specialized Elective I | 3-1-0-4 | - |
| VII | CS702 | Specialized Elective II | 3-1-0-4 | - |
| VII | CS703 | Specialized Elective III | 3-1-0-4 | - |
| VIII | CS801 | Specialized Elective IV | 3-1-0-4 | - |
| VIII | CS802 | Specialized Elective V | 3-1-0-4 | - |
| VIII | CS803 | Specialized Elective VI | 3-1-0-4 | - |
Advanced Departmental Elective Courses
The department offers a range of advanced elective courses tailored to meet evolving industry demands and student interests. These courses are designed to deepen understanding, enhance practical skills, and encourage innovation.
Deep Learning with TensorFlow: This course introduces students to neural network architectures such as CNNs, RNNs, LSTMs, and Transformers. Through hands-on labs using TensorFlow, students learn how to build and train complex models for image recognition, natural language processing, and time series forecasting.
Advanced Cryptography: Students explore modern cryptographic techniques including symmetric and asymmetric encryption algorithms, digital signatures, hash functions, and blockchain technology. The course emphasizes secure protocol design and implementation, preparing students for careers in cybersecurity and data protection.
Reinforcement Learning Algorithms: This course covers theoretical foundations of reinforcement learning, including Markov Decision Processes, Q-learning, policy gradients, and actor-critic methods. Practical applications include robotics control, game AI, autonomous vehicles, and recommendation systems.
DevOps and CI/CD Pipelines: Students learn to implement continuous integration and deployment pipelines using tools like Jenkins, GitLab CI, Docker, and Kubernetes. The course emphasizes automation, testing strategies, infrastructure as code, and cloud-native development practices.
Quantum Computing Fundamentals: This emerging field explores quantum algorithms, superposition, entanglement, and quantum error correction. Students gain exposure to quantum programming using platforms like IBM Qiskit and Microsoft Azure Quantum.
Computer Vision and Image Processing: The course covers image filtering, segmentation, object detection, and facial recognition using OpenCV and deep learning frameworks. Applications include medical imaging, surveillance systems, augmented reality, and autonomous navigation.
Natural Language Processing with Transformers: Students study transformer architectures, BERT models, language modeling, and text generation techniques. Real-world applications include chatbots, sentiment analysis, machine translation, and content summarization.
Mobile App Development using Flutter: This course teaches cross-platform app development using Flutter SDK. Students learn UI/UX design principles, state management, API integration, and deployment on iOS and Android platforms.
Blockchain and Smart Contracts: The curriculum explores blockchain consensus mechanisms, smart contract development with Solidity, Ethereum, and other platforms. Practical labs involve creating decentralized applications (dApps) and exploring use cases in finance, supply chain, and digital identity.
Advanced Database Systems: This course delves into NoSQL databases, graph databases, distributed systems, and data warehousing. Students implement scalable solutions using technologies like Cassandra, Neo4j, MongoDB, and Apache Spark.
Project-Based Learning Philosophy
The department's philosophy on project-based learning emphasizes experiential education that bridges the gap between theory and application. Projects are structured to mirror real-world engineering challenges, encouraging creativity, collaboration, and problem-solving skills.
Mini-projects begin in the second year, allowing students to explore specific areas of interest under faculty guidance. These projects are evaluated based on innovation, technical execution, presentation quality, and peer feedback. Students form teams and work collaboratively throughout the semester, simulating professional environments.
The final-year capstone project is a comprehensive endeavor that integrates all learned concepts into a full-fledged solution addressing an actual societal or industrial problem. Projects are selected through a proposal process involving faculty mentors who guide students through research, design, development, testing, and documentation phases.
Evaluation criteria include feasibility of the solution, impact assessment, technical depth, presentation skills, and adherence to deadlines. The final project is presented publicly before a panel of experts including industry professionals and academic staff, fostering a culture of transparency, accountability, and excellence.