Comprehensive Course Structure

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

Advanced Departmental Electives

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.

Machine Learning

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.

Cybersecurity

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.

Big Data Analytics

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.

Cloud Computing

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.

Computer Graphics

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.

IoT and Smart Devices

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.

Reinforcement Learning

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.

Neural Networks

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.

DevOps Practices

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.

Robotics and Automation

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.

Project-Based Learning Philosophy

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

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.

Final-Year Thesis/Capstone Project

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.

Evaluation Criteria

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.

Project Selection Process

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.