Comprehensive Curriculum Overview
The Computer Science and Engineering program at Babu Sunder Singh Institute of Technology and Management is meticulously structured to ensure a balanced progression from foundational knowledge to advanced specialization. The curriculum spans eight semesters, integrating core theoretical concepts with practical applications through laboratory work and real-world projects.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | PHYS101 | Physics for Engineers | 3-1-0-4 | None |
| I | MATH101 | Calculus and Differential Equations | 4-0-0-4 | None |
| I | CSE101 | Introduction to Programming | 3-0-2-5 | None |
| I | CHM101 | Chemistry for Engineers | 3-1-0-4 | None |
| I | ENG101 | English for Technical Communication | 2-0-0-2 | None |
| I | PHYS102 | Engineering Physics Lab | 0-0-3-1 | PHYS101 |
| I | CSE102 | Programming Lab | 0-0-3-1 | CSE101 |
| II | MATH201 | Linear Algebra and Probability | 4-0-0-4 | MATH101 |
| II | CSE201 | Data Structures and Algorithms | 3-1-0-4 | CSE101 |
| II | ECE201 | Electrical Circuits and Networks | 3-1-0-4 | PHYS101 |
| II | CSE202 | Database Management Systems | 3-1-0-4 | CSE101 |
| II | CSE203 | Computer Organization and Architecture | 3-1-0-4 | CSE101 |
| II | ECE202 | Electronic Devices and Circuits | 3-1-0-4 | PHYS101 |
| III | CSE301 | Operating Systems | 3-1-0-4 | CSE201, CSE203 |
| III | CSE302 | Computer Networks | 3-1-0-4 | CSE201 |
| III | CSE303 | Software Engineering | 3-1-0-4 | CSE202 |
| III | CSE304 | Artificial Intelligence | 3-1-0-4 | CSE201, MATH201 |
| III | CSE305 | Object-Oriented Programming with Java | 3-1-0-4 | CSE101 |
| III | CSE306 | Algorithms Design and Analysis Lab | 0-0-3-1 | CSE201 |
| IV | CSE401 | Human-Computer Interaction | 3-1-0-4 | CSE303 |
| IV | CSE402 | Distributed Systems | 3-1-0-4 | CSE301, CSE302 |
| IV | CSE403 | Security and Cryptography | 3-1-0-4 | CSE301 |
| IV | CSE404 | Big Data Analytics | 3-1-0-4 | CSE304 |
| IV | CSE405 | Embedded Systems | 3-1-0-4 | CSE203 |
| IV | CSE406 | Distributed Systems Lab | 0-0-3-1 | CSE301, CSE302 |
| V | CSE501 | Machine Learning | 3-1-0-4 | CSE304, MATH201 |
| V | CSE502 | Reinforcement Learning | 3-1-0-4 | CSE501 |
| V | CSE503 | Natural Language Processing | 3-1-0-4 | CSE501 |
| V | CSE504 | Computer Vision | 3-1-0-4 | CSE501 |
| V | CSE505 | Deep Learning with TensorFlow | 3-1-0-4 | CSE501 |
| V | CSE506 | AI and Robotics Lab | 0-0-3-1 | CSE501 |
| VI | CSE601 | Network Security | 3-1-0-4 | CSE302, CSE303 |
| VI | CSE602 | Cybersecurity Management | 3-1-0-4 | CSE601 |
| VI | CSE603 | Malware Analysis and Forensics | 3-1-0-4 | CSE303 |
| VI | CSE604 | Penetration Testing | 3-1-0-4 | CSE601 |
| VI | CSE605 | Security Architecture | 3-1-0-4 | CSE601 |
| VI | CSE606 | Cybersecurity Lab | 0-0-3-1 | CSE601 |
| VII | CSE701 | Cloud Computing | 3-1-0-4 | CSE301, CSE302 |
| VII | CSE702 | DevOps Practices | 3-1-0-4 | CSE303 |
| VII | CSE703 | Big Data Technologies | 3-1-0-4 | CSE404 |
| VII | CSE704 | Mobile Application Development | 3-1-0-4 | CSE305 |
| VII | CSE705 | Web Technologies and Frameworks | 3-1-0-4 | CSE305 |
| VII | CSE706 | Full Stack Development Lab | 0-0-3-1 | CSE305 |
| VIII | CSE801 | Capstone Project | 3-1-0-4 | All previous courses |
| VIII | CSE802 | Thesis Work | 0-0-3-1 | None |
| VIII | CSE803 | Internship | 0-0-3-1 | All previous courses |
Detailed Course Descriptions for Departmental Electives
Machine Learning: This course introduces students to the core concepts of machine learning, including supervised and unsupervised learning algorithms, neural networks, and deep learning architectures. Students will learn to implement models using Python libraries such as scikit-learn, TensorFlow, and Keras. The curriculum includes hands-on projects involving real-world datasets, enabling students to gain practical experience in model selection, evaluation, and deployment.
Reinforcement Learning: Reinforcement learning is a subset of machine learning where agents learn to make decisions by interacting with an environment to maximize cumulative rewards. This course covers Markov Decision Processes (MDPs), Q-learning, policy gradients, and deep reinforcement learning techniques. Students will implement algorithms on simulated environments and explore applications in robotics, game playing, and autonomous systems.
Natural Language Processing: Natural language processing (NLP) deals with the interaction between computers and human languages. This course explores text preprocessing, sentiment analysis, named entity recognition, machine translation, and question answering systems. Students will utilize tools like NLTK, spaCy, and Hugging Face Transformers to build NLP pipelines that can process and generate human-like language.
Computer Vision: Computer vision involves enabling machines to interpret and understand visual information from the world. The course covers image processing techniques, feature extraction, object detection, segmentation, and recognition algorithms. Students will implement computer vision models using OpenCV, PyTorch, and TensorFlow, focusing on real-world applications such as facial recognition, autonomous driving, and medical imaging.
Deep Learning with TensorFlow: This course provides an in-depth exploration of deep learning frameworks, particularly TensorFlow. Students will learn to design, train, and optimize neural networks for various tasks including classification, regression, and generation. The curriculum includes convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, with emphasis on practical implementation using real datasets.
Network Security: Network security focuses on protecting network infrastructure from unauthorized access and attacks. This course covers fundamental concepts such as firewalls, intrusion detection systems, secure protocols, and encryption techniques. Students will gain hands-on experience through labs involving packet analysis, vulnerability assessment, and penetration testing using tools like Wireshark, Metasploit, and Nmap.
Cybersecurity Management: Cybersecurity management addresses the strategic aspects of protecting digital assets within organizations. This course explores risk management frameworks, compliance standards (e.g., ISO 27001), incident response procedures, and security governance models. Students will develop skills in conducting security audits, designing secure architectures, and managing cybersecurity teams.
Malware Analysis and Forensics: Malware analysis involves examining malicious software to understand its behavior and potential impact. This course teaches students how to reverse engineer malware, perform static and dynamic analysis, and extract indicators of compromise (IOCs). Labs include working with disassemblers like IDA Pro and debugging tools such as x64dbg, preparing students for careers in cybersecurity forensics.
Penetration Testing: Penetration testing simulates cyberattacks to identify vulnerabilities in systems and networks. This course provides a comprehensive overview of ethical hacking methodologies, including reconnaissance, scanning, exploitation, and reporting. Students will practice penetration testing using tools like Kali Linux, Burp Suite, and SQLMap, gaining real-world experience in assessing network security.
Security Architecture: Security architecture focuses on designing robust and scalable security frameworks for enterprise environments. This course covers design principles, threat modeling, access control mechanisms, and secure coding practices. Students will learn to evaluate existing systems for security gaps and propose architectural improvements based on industry standards and best practices.
Project-Based Learning Philosophy
At Babu Sunder Singh Institute of Technology and Management, project-based learning is central to the Computer Science and Engineering program. The philosophy emphasizes experiential learning, where students apply theoretical knowledge to solve real-world problems. Projects are designed to mirror industry practices, encouraging innovation, teamwork, and critical thinking.
Mini-projects are introduced from the second year onwards, allowing students to explore specific topics in depth. These projects typically involve small teams (3-5 members) and last for one semester. Students select their project themes based on faculty guidance and personal interests, ensuring relevance and engagement.
The final-year thesis or capstone project represents the culmination of the program. Students work closely with faculty mentors to define research questions, design methodologies, and develop innovative solutions. Projects are often sponsored by industry partners, providing students with exposure to real-world challenges and potential career opportunities.
Evaluation criteria for projects include technical depth, innovation, presentation quality, and team collaboration. Regular progress reviews ensure that students stay on track and receive timely feedback. The program also encourages participation in national and international competitions, further enhancing student engagement and recognition.