Computer Science & Engineering (Artificial Intelligence and Machine Learning)
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The B.Tech program in Computer Science & Engineering (Artificial Intelligence & Machine Learning), established in 2023, is designed to provide students with a strong foundation in computer science along with specialized knowledge in AI, Machine Learning, Data Science, and Intelligent Systems. The program emphasizes practical learning through hands-on labs, projects, and internships, preparing students for real-world problem solving and industry-ready skills. Students gain exposure to programming languages like Python and R, as well as tools such as TensorFlow and PyTorch, while also learning ethical AI practices and intelligent system design. Core subjects include AI & Intelligent Agents, ML & Deep Learning, Data Structures & Algorithms, Data Science & Big Data, NLP & Computer Vision, IoT & Cloud AI Platforms, and Software Engineering. Graduates can pursue careers as AI/ML Engineers, Data Scientists, Software Developers, BI Analysts, or opt for research and higher studies. The program aims to create innovative, socially responsible, and industry-ready engineers capable of contributing to intelligent and sustainable technological solutions.
Vision
To be a globally recognized centre of excellence in Artificial Intelligence and Machine Learning education and research, fostering innovation, ethical AI practices, entrepreneurship, and sustainable technological solutions for societal advancement.
Mission
- To impart quality education in AI & ML through outcome-based learning, hands-on training, and interdisciplinary collaboration in emerging computing technologies.
- To promote research, innovation, and product development in Artificial Intelligence, Machine Learning, Data Science, and allied domains to address real-world and societal challenges.
- To inculcate ethical AI practices, professional responsibility, and lifelong learning skills to meet global industry and research standards.
- To strengthen industry–academia collaboration and entrepreneurship for sustainable technological development and knowledge transfer.
| No. Seats | Duration | Eligibility Criteria |
| 60 | 4 Years | 12th Pass General/ OBC Category Student Having 45% Marks in PCM. 12th Pass SC/ST Category Students Having 40% marks in PCM. |
- Java/ C++/ .Net Laboratory
- Operating System Laboratory
- Computer Networking Laboratory
- Computer Graphics & Multimedia Laboratory
- Database Management System Laboratory
- Soft Computing Laboratory
- Software Engineering & Project Management Laboratory
Semester | Syllabus | Scheme |
B.Tech CSE with AI & ML III | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/AI_and_Machine_Learning170921041538.pdf | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/BTechAIML131022123605.pdf |
Iv | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/IV_sem_BTech_AIML230222083345.pdf | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/AIML_IV_sem220422043332.pdf |
V | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/B%20Tech%20V%20sem%20CSE%20AIML%20syllabus010223052406.pdf | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/B%20Tech%20V%20sem%20CSE-AIML%20scheme020223050944.pdf |
Vi | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/VI_Sem_AIML_SY170123114104.pdf | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/AIML%20VI%20sem%20scheme060123014425.pdf |
Vii | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/CSE-AIML%20VII%20sem%20syllabus090823122849.pdf | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/BTech%20CSE-AIML%20110723020925.pdf |
viii | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/AIML%20VIII%20sem%20Syllabus%201070224045203.pdf | https://www.rgpv.ac.in/UC/frm_download_file.aspx?Filepath=CDN/PubContent/Scheme/AIML%20VIII%20sem%20Scheme090124023321.pdf |
PO1: Engineering Knowledge
Apply knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to solve complex engineering problems and develop appropriate technical solutions.
PO2: Problem Analysis
Identify, formulate, review research literature, and analyze complex engineering problems using first principles of mathematics, natural sciences, and engineering sciences to arrive at substantiated conclusions.
PO3: Design / Development of Solutions
Design solutions for complex engineering problems and design system components or processes that meet specified needs with appropriate consideration for public health, safety, cultural, societal, and environmental factors.
PO4: Conduct Investigations of Complex Problems
Use research-based knowledge and methods, including design of experiments, data analysis, and interpretation of results, to investigate complex engineering problems and provide valid conclusions.
PO5: Modern Tool Usage
Create, select, and apply appropriate modern engineering and IT tools—including modeling, simulation, and data analytics tools—with an understanding of their limitations.
PO6: The Engineer and Society
Apply contextual knowledge to assess societal, health, safety, legal, and cultural issues and the responsibilities relevant to professional engineering practice.
PO7: Environment and Sustainability
Understand the impact of engineering solutions in societal and environmental contexts and demonstrate knowledge of sustainable development principles.
PO8: Ethics
Apply ethical principles, professional ethics, and responsibilities and adhere to engineering norms and standards in professional practice.
PO9: Individual and Team Work
Function effectively as an individual, and as a member or leader in diverse and multidisciplinary teams.
PO10: Communication
Communicate effectively on complex engineering activities with the engineering community and society at large through reports, presentations, documentation, and clear instructions.
PO11: Project Management and Finance
Demonstrate knowledge and understanding of engineering management principles and apply these to manage projects in multidisciplinary environments.
PO12: Life-Long Learning
Recognize the need for, and engage in, independent and life-long learning in the broadest context of technological change.
PEO1: Graduates will build successful careers in AI, Data Science, Software, and allied technology sectors through strong technical and professional competencies.
PEO2: Graduates will pursue higher education, research, innovation, or entrepreneurial ventures in advanced computing domains.
PEO3: Graduates will demonstrate leadership, effective communication, teamwork, and ethical practices in multidisciplinary professional environments.
PEO4: Graduates will engage in lifelong learning to adapt to rapidly evolving AI technologies and contribute responsibly to society.
PSO 1: AI & ML Model Development
Graduates will be able to design, develop, and deploy intelligent systems using machine learning algorithms, deep learning, data analytics, and optimization techniques.
PSO 2: Data-Driven Decision Systems
Graduates will be able to analyze large-scale structured and unstructured data, build predictive models, and implement AI solutions using modern tools and cloud platforms.
PSO 3: Ethical AI & Professional Practice
Graduates will demonstrate ethical AI implementation, cybersecurity awareness, teamwork, communication skills, and entrepreneurial mindset to solve complex engineering problems responsibly.