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Fees
₹1,80,000
Placement
92.5%
Avg Package
₹6,50,000
Highest Package
₹12,00,000
Fees
₹1,80,000
Placement
92.5%
Avg Package
₹6,50,000
Highest Package
₹12,00,000
Seats
150
Students
2,000
Seats
150
Students
2,000
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | CS101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | CS102 | Basic Electronics | 3-1-0-4 | None |
| 1 | CS103 | Programming in C | 2-0-2-3 | None |
| 1 | CS104 | Introduction to Computing | 2-0-0-2 | None |
| 1 | CS105 | Engineering Graphics | 3-0-0-3 | None |
| 1 | CS106 | English Communication Skills | 2-0-0-2 | None |
| 2 | CS201 | Engineering Mathematics II | 3-1-0-4 | CS101 |
| 2 | CS202 | Electronic Circuits | 3-1-0-4 | CS102 |
| 2 | CS203 | Data Structures Using C | 3-1-0-4 | CS103 |
| 2 | CS204 | Computer Organization | 3-1-0-4 | CS102 |
| 2 | CS205 | Object Oriented Programming | 3-1-0-4 | CS103 |
| 2 | CS206 | Physics for Computer Science | 3-0-0-3 | None |
| 3 | CS301 | Probability and Statistics | 3-1-0-4 | CS201 |
| 3 | CS302 | Database Management Systems | 3-1-0-4 | CS203 |
| 3 | CS303 | Operating Systems | 3-1-0-4 | CS204 |
| 3 | CS304 | Software Engineering | 3-1-0-4 | CS205 |
| 3 | CS305 | Computer Networks | 3-1-0-4 | CS204 |
| 3 | CS306 | Digital Logic Design | 3-1-0-4 | CS202 |
| 4 | CS401 | Design and Analysis of Algorithms | 3-1-0-4 | CS302 |
| 4 | CS402 | Web Technologies | 3-1-0-4 | CS205 |
| 4 | CS403 | Mobile Computing | 3-1-0-4 | CS305 |
| 4 | CS404 | Embedded Systems | 3-1-0-4 | CS204 |
| 4 | CS405 | Artificial Intelligence | 3-1-0-4 | CS301 |
| 4 | CS406 | Human Computer Interaction | 2-0-0-2 | CS304 |
| 5 | CS501 | Machine Learning | 3-1-0-4 | CS301 |
| 5 | CS502 | Cybersecurity | 3-1-0-4 | CS305 |
| 5 | CS503 | Big Data Analytics | 3-1-0-4 | CS302 |
| 5 | CS504 | Cloud Computing | 3-1-0-4 | CS305 |
| 5 | CS505 | Computer Graphics | 3-1-0-4 | CS205 |
| 5 | CS506 | IoT and Smart Devices | 3-1-0-4 | CS204 |
| 6 | CS601 | Advanced Data Structures | 3-1-0-4 | CS401 |
| 6 | CS602 | Advanced Algorithms | 3-1-0-4 | CS401 |
| 6 | CS603 | DevOps Practices | 3-1-0-4 | CS304 |
| 6 | CS604 | Reinforcement Learning | 3-1-0-4 | CS501 |
| 6 | CS605 | Neural Networks | 3-1-0-4 | CS501 |
| 6 | CS606 | Robotics and Automation | 3-1-0-4 | CS204 |
| 7 | CS701 | Research Methodology | 2-0-0-2 | None |
| 7 | CS702 | Capstone Project I | 3-0-0-3 | CS601 |
| 7 | CS703 | Capstone Project II | 3-0-0-3 | CS702 |
| 7 | CS704 | Industrial Training | 2-0-0-2 | None |
| 7 | CS705 | Project Proposal | 1-0-0-1 | None |
| 7 | CS706 | Project Presentation | 1-0-0-1 | CS705 |
| 8 | CS801 | Thesis Research | 3-0-0-3 | CS703 |
| 8 | CS802 | Final Project Report | 3-0-0-3 | CS801 |
The advanced departmental electives in the Computer Science and Engineering program at Truba Institute of Engineering and Information Technology Bhopal are designed to provide specialized knowledge and skills in emerging areas of technology. These courses are structured to bridge theoretical concepts with practical applications, preparing students for careers in high-demand sectors.
This course explores advanced machine learning techniques including supervised, unsupervised, and reinforcement learning algorithms. Students will learn to implement models using Python libraries such as Scikit-learn, TensorFlow, and Keras. The course covers topics like neural networks, deep learning architectures, natural language processing, and computer vision.
This elective delves into the principles of cybersecurity, focusing on network security, cryptography, ethical hacking, and incident response. Students will gain hands-on experience with tools such as Wireshark, Metasploit, Nmap, and Burp Suite. The course also addresses compliance frameworks like ISO 27001 and GDPR.
This course introduces students to big data technologies such as Hadoop, Spark, and NoSQL databases. Students will learn to process and analyze large datasets using distributed computing frameworks. The curriculum includes data visualization techniques, predictive modeling, and real-time analytics solutions.
This elective provides a comprehensive overview of cloud computing platforms and services. Students will explore virtualization, containerization, microservices architecture, and DevOps practices. The course covers AWS, Azure, and Google Cloud Platform (GCP) offerings and includes hands-on labs on deploying scalable applications.
This course focuses on the theory and practice of computer graphics, including 3D modeling, rendering techniques, animation principles, and interactive media development. Students will use industry-standard tools like Maya, Blender, and Unity to create visual effects and immersive experiences.
This elective explores the design and implementation of Internet of Things (IoT) systems using microcontrollers, sensors, and wireless communication protocols. Students will build smart devices for applications in agriculture, healthcare, and smart cities. The course includes hands-on labs with Arduino, Raspberry Pi, and ESP32 boards.
This advanced topic covers the mathematical foundations of reinforcement learning and its applications in robotics, game theory, and autonomous systems. Students will implement algorithms using simulation environments and learn to optimize decision-making processes under uncertainty.
This course examines the structure and function of neural networks, including feedforward, convolutional, and recurrent architectures. Students will develop deep learning models for image recognition, natural language processing, and time series forecasting using frameworks like TensorFlow and PyTorch.
This elective introduces students to continuous integration/continuous deployment (CI/CD) pipelines, containerization with Docker, orchestration with Kubernetes, and infrastructure as code using Terraform. The course emphasizes automation, monitoring, and collaboration in software development environments.
This course combines principles of mechanical engineering, electrical engineering, and computer science to design and program robots. Students will learn about sensors, actuators, control systems, and artificial intelligence in robotics applications. Labs include building and programming robotic arms, autonomous vehicles, and industrial automation systems.
The department's philosophy on project-based learning is rooted in the belief that students learn best when they engage in hands-on, real-world problem-solving activities. Projects are structured to encourage collaboration, critical thinking, and innovation while reinforcing theoretical knowledge with practical application.
Mini-projects begin in the third semester and continue through the fifth semester. These projects allow students to apply concepts learned in core courses to solve specific problems or develop prototypes. Each project is assigned a faculty mentor who guides students through the process of defining objectives, designing solutions, implementing code, testing results, and presenting findings.
The final-year capstone project is a significant undertaking that requires students to integrate knowledge from all previous semesters. Projects are typically chosen based on current industry trends or societal challenges. Students work closely with faculty mentors and often collaborate with industry partners or research institutions.
Projects are evaluated based on multiple criteria including technical proficiency, creativity, teamwork, documentation quality, and presentation skills. The final project report must include a literature review, methodology, implementation details, results analysis, and future work suggestions. Students present their projects in front of a panel of faculty members and industry experts.
Students select their projects during the fifth semester by submitting proposals to faculty mentors. Proposals are reviewed based on relevance, feasibility, innovation potential, and alignment with student interests. Faculty members guide students in refining their ideas and provide resources for successful completion.
