Course Structure Overview
The curriculum of the Masters Of Computer Science program at Dr B R Ambedkar Institute Of Technology Port Blair is meticulously designed to provide a comprehensive understanding of modern computing concepts and technologies. The program spans two academic years, divided into four semesters, with each semester consisting of core courses, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Advanced Data Structures | 3-1-0-4 | None |
| 1 | CS102 | Algorithm Design & Analysis | 3-1-0-4 | CS101 |
| 1 | CS103 | Mathematics for Computer Science | 3-1-0-4 | None |
| 1 | CS104 | Computer Organization & Architecture | 3-1-0-4 | None |
| 1 | CS105 | Operating Systems | 3-1-0-4 | CS104 |
| 1 | CS106 | Database Systems | 3-1-0-4 | CS102 |
| 1 | CS107 | Programming Paradigms | 3-1-0-4 | None |
| 1 | CS108 | Research Methodology | 2-0-0-2 | None |
| 2 | CS201 | Software Engineering | 3-1-0-4 | CS106 |
| 2 | CS202 | Computer Networks | 3-1-0-4 | CS104 |
| 2 | CS203 | Human-Computer Interaction | 3-1-0-4 | CS107 |
| 2 | CS204 | Mobile Computing | 3-1-0-4 | CS202 |
| 2 | CS205 | Cloud Computing | 3-1-0-4 | CS202 |
| 2 | CS206 | System Design | 3-1-0-4 | CS201 |
| 2 | CS207 | Statistics for Data Science | 3-1-0-4 | CS103 |
| 2 | CS208 | Project Planning & Management | 2-0-0-2 | CS201 |
| 3 | CS301 | Machine Learning | 3-1-0-4 | CS207 |
| 3 | CS302 | Deep Learning | 3-1-0-4 | CS301 |
| 3 | CS303 | Cryptography | 3-1-0-4 | CS102 |
| 3 | CS304 | Network Security | 3-1-0-4 | CS202 |
| 3 | CS305 | Data Mining | 3-1-0-4 | CS207 |
| 3 | CS306 | Computer Vision | 3-1-0-4 | CS301 |
| 3 | CS307 | Reinforcement Learning | 3-1-0-4 | CS301 |
| 3 | CS308 | Big Data Technologies | 3-1-0-4 | CS206 |
| 4 | CS401 | Capstone Project | 6-0-0-6 | CS301, CS305 |
| 4 | CS402 | Research Thesis | 6-0-0-6 | CS208 |
| 4 | CS403 | Advanced Topics in AI | 3-1-0-4 | CS301 |
| 4 | CS404 | Advanced Cybersecurity | 3-1-0-4 | CS303 |
| 4 | CS405 | Advanced Data Analytics | 3-1-0-4 | CS305 |
| 4 | CS406 | Software Architecture | 3-1-0-4 | CS201 |
| 4 | CS407 | Internet of Things | 3-1-0-4 | CS204 |
| 4 | CS408 | Quantum Computing | 3-1-0-4 | CS103 |
Advanced Departmental Elective Courses
The department offers a wide range of advanced departmental elective courses designed to deepen students' understanding of specialized areas within computer science. These courses are tailored to meet the evolving needs of the industry and academic research.
Machine Learning: This course provides an in-depth exploration of machine learning algorithms, including supervised and unsupervised learning techniques, neural networks, and deep learning models. Students will learn to implement these algorithms using Python and TensorFlow, and apply them to real-world datasets. The course emphasizes both theoretical foundations and practical applications, preparing students for careers in AI research and development.
Deep Learning: Building upon the concepts introduced in Machine Learning, this course delves into advanced neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students will work on projects involving image recognition, natural language processing, and generative models, gaining hands-on experience with cutting-edge tools and frameworks.
Cryptography: This course covers the principles and techniques of modern cryptography, including symmetric and asymmetric encryption, hash functions, digital signatures, and public key infrastructure. Students will study both classical and contemporary cryptographic protocols and analyze their security properties. The course includes practical sessions on implementing cryptographic algorithms and conducting security assessments.
Network Security: This course explores the fundamental concepts of network security, including firewalls, intrusion detection systems, and secure communication protocols. Students will learn to identify vulnerabilities in network infrastructures and develop strategies to mitigate risks. The course includes hands-on labs where students simulate attacks and defend against them using industry-standard tools.
Data Mining: This course focuses on extracting useful patterns and knowledge from large datasets using various data mining techniques. Students will learn about clustering, classification, association rule mining, and anomaly detection. The course emphasizes practical applications in business intelligence, healthcare, and scientific research.
Computer Vision: This course introduces students to the fundamentals of computer vision, including image processing, feature extraction, and object recognition. Students will work on projects involving facial recognition, image segmentation, and video analysis, using libraries like OpenCV and TensorFlow.
Reinforcement Learning: This course covers the theory and practice of reinforcement learning, including Markov decision processes, Q-learning, policy gradients, and actor-critic methods. Students will implement reinforcement learning agents in simulated environments and apply them to real-world problems such as robotics and game playing.
Big Data Technologies: This course provides an overview of big data processing frameworks such as Hadoop, Spark, and Kafka. Students will learn to process and analyze large datasets using distributed computing techniques and apply these skills to real-world scenarios in various domains.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a cornerstone of effective education. This approach enables students to apply theoretical knowledge to real-world problems, fostering creativity, critical thinking, and collaboration skills.
Mini-projects are integrated throughout the program, beginning in the first semester. These projects are designed to reinforce concepts learned in core courses and encourage students to explore practical applications. Each project is assigned a mentor from the faculty, who provides guidance and feedback throughout the process.
The final-year capstone project is a comprehensive endeavor that allows students to demonstrate their mastery of the field. Students select a project topic in consultation with faculty mentors, ensuring alignment with current industry trends and research interests. The project involves extensive research, development, and testing phases, culminating in a final presentation and documentation.
Projects are evaluated based on multiple criteria, including technical proficiency, innovation, teamwork, and presentation skills. Students are encouraged to present their work at conferences, publish papers, or submit patents, further enhancing their academic and professional profiles.