Comprehensive Course Listing
The following table provides a detailed listing of all core, departmental elective, science elective, and lab courses offered across the 8 semesters of the Computer Science and Engineering program at Institute of Engineering and Technology Lucknow.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | CS102 | Programming Fundamentals | 3-0-2-4 | - |
| 1 | CS103 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CS104 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | CS105 | Basic Electrical Engineering | 3-1-0-4 | - |
| 2 | CS201 | Engineering Mathematics II | 3-1-0-4 | CS101 |
| 2 | CS202 | Data Structures and Algorithms | 3-1-0-4 | CS102 |
| 2 | CS203 | Digital Logic Design | 3-1-0-4 | CS105 |
| 2 | CS204 | Database Management Systems | 3-1-0-4 | CS202 |
| 2 | CS205 | Computer Organization | 3-1-0-4 | CS203 |
| 3 | CS301 | Operating Systems | 3-1-0-4 | CS205 |
| 3 | CS302 | Computer Networks | 3-1-0-4 | CS205 |
| 3 | CS303 | Software Engineering | 3-1-0-4 | CS202 |
| 3 | CS304 | Artificial Intelligence | 3-1-0-4 | CS202 |
| 3 | CS305 | Cybersecurity | 3-1-0-4 | CS205 |
| 4 | CS401 | Web Technologies | 3-1-0-4 | CS303 |
| 4 | CS402 | Mobile Application Development | 3-1-0-4 | CS303 |
| 4 | CS403 | Big Data Technologies | 3-1-0-4 | CS204 |
| 4 | CS404 | Advanced Algorithms | 3-1-0-4 | CS202 |
| 4 | CS405 | Distributed Systems | 3-1-0-4 | CS302 |
| 5 | CS501 | Deep Learning | 3-1-0-4 | CS304 |
| 5 | CS502 | Natural Language Processing | 3-1-0-4 | CS501 |
| 5 | CS503 | Computer Vision | 3-1-0-4 | CS501 |
| 5 | CS504 | Cryptography and Network Security | 3-1-0-4 | CS305 |
| 5 | CS505 | Reinforcement Learning | 3-1-0-4 | CS501 |
| 6 | CS601 | Microcontroller Programming | 3-1-0-4 | CS203 |
| 6 | CS602 | Sensors and Actuators | 3-1-0-4 | CS601 |
| 6 | CS603 | Real-Time Systems | 3-1-0-4 | CS601 |
| 6 | CS604 | IoT Platforms | 3-1-0-4 | CS602 |
| 6 | CS605 | Smart City Technologies | 3-1-0-4 | CS604 |
| 7 | CS701 | Cloud Architecture | 3-1-0-4 | CS301 |
| 7 | CS702 | DevOps and CI/CD | 3-1-0-4 | CS701 |
| 7 | CS703 | Microservices Design | 3-1-0-4 | CS702 |
| 7 | CS704 | Containerization with Docker and Kubernetes | 3-1-0-4 | CS703 |
| 7 | CS705 | Enterprise Software Development | 3-1-0-4 | CS704 |
| 8 | CS801 | Advanced Research Project | 2-0-2-4 | - |
| 8 | CS802 | Capstone Thesis | 2-0-2-4 | - |
| 8 | CS803 | Industry Internship | 0-0-0-6 | - |
Advanced Departmental Elective Courses
The following section outlines detailed descriptions of advanced departmental elective courses offered in the program, highlighting their learning objectives and relevance to current industry trends.
Deep Learning (CS501)
This course introduces students to deep neural networks, convolutional networks, recurrent networks, and transformer architectures. Students will implement models for image classification, object detection, sequence modeling, and natural language processing tasks using frameworks like TensorFlow and PyTorch.
Natural Language Processing (CS502)
Students explore the intersection of linguistics and machine learning through advanced NLP techniques. Topics include word embeddings, sentiment analysis, named entity recognition, machine translation, and question answering systems.
Computer Vision (CS503)
This course covers image processing, feature extraction, object detection, segmentation, and 3D reconstruction using computer vision algorithms. Practical applications include facial recognition, autonomous driving, and medical imaging.
Cryptography and Network Security (CS504)
Students learn cryptographic principles, secure protocols, and network security mechanisms. The course includes hands-on labs on encryption techniques, digital signatures, firewall configurations, and penetration testing tools.
Reinforcement Learning (CS505)
This advanced course explores reinforcement learning algorithms such as Q-learning, policy gradients, and actor-critic methods. Students will implement agents for complex environments using OpenAI Gym and custom RL frameworks.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means to foster deep understanding and practical application of theoretical concepts. The curriculum includes mandatory mini-projects in the second and third years, followed by a comprehensive final-year thesis or capstone project.
Mini-Projects (Semesters 2 & 3)
In the second semester, students work on small-scale projects involving data structures and algorithms. In the third semester, they tackle more complex problems in software engineering or embedded systems design.
Final-Year Thesis/Capstone Project
The final-year project allows students to apply their accumulated knowledge to a real-world challenge under faculty supervision. Students can choose from industry-sponsored projects, research topics, or entrepreneurial ventures. The evaluation process includes a proposal defense, mid-term progress report, and final presentation.
Project selection is facilitated through a mentorship system, where each student is paired with a faculty advisor based on mutual interest. The department also organizes an annual Design for Impact competition to encourage innovative solutions addressing societal needs.