Comprehensive Course Listing
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | CSE101 | Introduction to Programming | 3-0-0-3 | - |
| I | MAT101 | Calculus I | 3-0-0-3 | - |
| I | PHY101 | Physics for Engineers | 3-0-0-3 | - |
| I | CSE102 | Computer Organization | 3-0-0-3 | - |
| I | ENG101 | English Communication | 2-0-0-2 | - |
| I | LIT101 | Liberal Arts | 2-0-0-2 | - |
| II | CSE201 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
| II | MAT201 | Calculus II | 3-0-0-3 | MAT101 |
| II | PHY201 | Electromagnetic Fields | 3-0-0-3 | PHY101 |
| II | CSE202 | Digital Logic Design | 3-0-0-3 | CSE102 |
| II | ENG201 | Technical Writing | 2-0-0-2 | - |
| III | CSE301 | Database Management Systems | 3-0-0-3 | CSE201 |
| III | MAT301 | Linear Algebra | 3-0-0-3 | MAT201 |
| III | CSE302 | Operating Systems | 3-0-0-3 | CSE202 |
| III | CSE303 | Computer Networks | 3-0-0-3 | CSE202 |
| III | ENG301 | Communication Skills | 2-0-0-2 | - |
| IV | CSE401 | Software Engineering | 3-0-0-3 | CSE301 |
| IV | MAT401 | Probability and Statistics | 3-0-0-3 | MAT301 |
| IV | CSE402 | Web Technologies | 3-0-0-3 | CSE301 |
| IV | CSE403 | Compiler Design | 3-0-0-3 | CSE302 |
| IV | ENG401 | Presentation Skills | 2-0-0-2 | - |
| V | CSE501 | Artificial Intelligence | 3-0-0-3 | CSE401 |
| V | CSE502 | Data Mining | 3-0-0-3 | MAT401 |
| V | CSE503 | Cybersecurity Fundamentals | 3-0-0-3 | CSE303 |
| V | CSE504 | Cloud Computing | 3-0-0-3 | CSE402 |
| V | ENG501 | Leadership and Ethics | 2-0-0-2 | - |
| VI | CSE601 | Machine Learning | 3-0-0-3 | CSE501 |
| VI | CSE602 | Natural Language Processing | 3-0-0-3 | CSE502 |
| VI | CSE603 | Big Data Analytics | 3-0-0-3 | CSE502 |
| VI | CSE604 | Distributed Systems | 3-0-0-3 | CSE403 |
| VI | ENG601 | Project Management | 2-0-0-2 | - |
| VII | CSE701 | Advanced Algorithms | 3-0-0-3 | CSE501 |
| VII | CSE702 | Deep Learning | 3-0-0-3 | CSE601 |
| VII | CSE703 | Computer Vision | 3-0-0-3 | CSE702 |
| VII | CSE704 | Blockchain Technology | 3-0-0-3 | CSE503 |
| VII | ENG701 | Research Methodology | 2-0-0-2 | - |
| VIII | CSE801 | Capstone Project | 3-0-0-3 | CSE701 |
| VIII | CSE802 | Internship | 0-0-0-6 | - |
| VIII | ENG801 | Professional Ethics | 2-0-0-2 | - |
Detailed Course Descriptions
Artificial Intelligence: This course explores the fundamentals of AI, including problem-solving techniques, search algorithms, knowledge representation, and reasoning. Students will learn to build intelligent systems that can make decisions based on data and logic.
Data Mining: This course introduces students to data mining techniques used in extracting patterns from large datasets. Topics include association rule mining, clustering, classification, and anomaly detection.
Cybersecurity Fundamentals: Students will understand the principles of cybersecurity, including network security, cryptography, system vulnerabilities, and risk management strategies.
Cloud Computing: This course provides an overview of cloud computing concepts, architectures, deployment models, and service models. Practical exercises involve setting up cloud environments using AWS and Azure.
Machine Learning: Students will explore supervised and unsupervised learning algorithms, neural networks, decision trees, support vector machines, and ensemble methods. Hands-on projects include building predictive models.
Natural Language Processing: This course covers NLP techniques for text processing, sentiment analysis, language modeling, and information extraction using tools like NLTK and spaCy.
Big Data Analytics: Students will learn to analyze large datasets using frameworks like Hadoop and Spark. The course includes data preprocessing, visualization, and advanced analytics techniques.
Distributed Systems: This course examines the design and implementation of distributed systems, including middleware, fault tolerance, consensus algorithms, and scalability challenges.
Advanced Algorithms: Advanced topics in algorithmic design, including dynamic programming, greedy algorithms, graph algorithms, and complexity theory. Students will solve complex computational problems.
Deep Learning: This course covers deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students will implement models using TensorFlow and PyTorch.
Computer Vision: Students will learn to develop computer vision applications using techniques like image segmentation, object detection, and facial recognition with frameworks like OpenCV and Keras.
Blockchain Technology: This course explores blockchain fundamentals, consensus mechanisms, smart contracts, and decentralized applications. Practical exercises involve building simple blockchains using Python.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is centered around experiential education that bridges theory and practice. Students engage in mini-projects during their second and third years, which are assessed through presentations, documentation, and peer reviews. These projects allow students to apply concepts learned in class to real-world scenarios.
During the final year, students undertake a capstone project under the guidance of faculty mentors. The scope of these projects is broad, ranging from developing an AI-powered recommendation system to creating a secure cloud-based platform for small businesses. Students are encouraged to collaborate with industry partners or start their own ventures.
The evaluation criteria for both mini-projects and capstone projects include technical execution, innovation, documentation quality, presentation skills, and teamwork. Faculty members mentor students throughout the process, providing feedback and resources to ensure successful completion.