Course Listing Across 8 Semesters

Advanced Departmental Elective Courses

These courses provide in-depth knowledge and practical skills in specialized areas of computer science and engineering:

• Machine Learning (CS501): This course explores supervised and unsupervised learning techniques, including regression, classification, clustering, and deep learning architectures. Students will implement models using Python libraries like Scikit-learn, TensorFlow, and PyTorch.
• Big Data Analytics (CS502): Students learn about Hadoop ecosystem, Spark, data warehousing, ETL processes, and real-time analytics platforms. The course includes hands-on labs with Apache Kafka and Elasticsearch.
• Cloud Computing (CS503): Focuses on cloud service models (IaaS, PaaS, SaaS), virtualization technologies, containerization with Docker and Kubernetes, and deployment strategies for scalable applications.
• Internet of Things (CS504): Covers sensor networks, wireless communication protocols, embedded systems programming, and integration with cloud platforms. Includes lab sessions on Arduino and Raspberry Pi.
• Embedded Systems (CS505): Students study microcontroller architectures, real-time operating systems, device drivers, and hardware-software co-design principles using ARM Cortex-M processors.
• Project Management (CS506): Introduces agile methodologies, risk management, resource allocation, and project lifecycle phases. Uses tools like Jira, Trello, and MS Project for simulations.
• Advanced Computer Architecture (CS601): Delves into pipeline design, memory hierarchy, cache optimization, instruction-level parallelism, and multicore architectures using MIPS and ARM instruction sets.
• Neural Networks (CS602): Explores feedforward networks, recurrent neural networks, convolutional neural networks, and generative adversarial networks. Includes practical implementation using TensorFlow and Keras.
• Distributed Systems (CS603): Covers distributed algorithms, consensus protocols, fault tolerance, and scalability challenges in large-scale systems. Labs involve building decentralized applications with Node.js and Go.
• Computer Vision (CS604): Focuses on image processing, feature extraction, object detection, and scene understanding using OpenCV and deep learning frameworks. Includes projects involving real-world datasets like COCO and ImageNet.
• Quantum Computing (CS605): Introduces quantum algorithms, qubit manipulation, error correction codes, and quantum programming with Qiskit and Cirq. Includes theoretical and simulation-based labs.
• Software Testing and Quality Assurance (CS606): Covers test automation frameworks, performance testing tools, security testing methodologies, and continuous integration pipelines using Selenium and Jenkins.

Project-Based Learning Philosophy

The department emphasizes project-based learning to bridge the gap between theory and practice. Students begin with small-scale projects in early semesters, gradually progressing to complex, interdisciplinary tasks. Mini-projects (CS702) are assigned in the seventh semester and involve working in teams on open-ended problems with industry mentors.

The final-year thesis/capstone project (CS801) is a comprehensive endeavor where students select topics aligned with their interests or industry needs. Faculty mentors guide them through research, development, documentation, and presentation stages. Projects are evaluated based on technical depth, innovation, impact, and clarity of communication.

Students can also participate in national competitions like the National Institute of Technology (NIT) Hackathon, ACM International Collegiate Programming Contest (ICPC), and IEEE competitions to gain recognition and practical experience.