The B.Tech. in Computer Science & Artificial Intelligence is designed for students seeking to develop the next generation of intelligent systems. Built on a strong foundation of computer science, the programme integrates machine learning, deep learning, natural language processing, computer vision, optimization, and generative AI to equip students with the knowledge required to build AI-driven technologies.
Students learn not only how AI models work, but also how to design, train, deploy, evaluate, and scale them in real-world environments. The curriculum combines mathematical rigor, computational thinking, and hands-on experimentation, enabling students to work across domains such as intelligent automation, predictive analytics, autonomous systems, conversational AI, healthcare, finance, and emerging digital technologies. Through industry engagement, research opportunities, and interdisciplinary projects, graduates are prepared to contribute to the rapidly evolving AI ecosystem.
Eligibility Criteria
- Minimum of 65% in Mathematics, Physics, and Chemistry (individually).
- At least 60% aggregate in PUC/12th or an equivalent examination from a recognized board.
- Valid score in JEE, KCET, COMEDK or any other state entrance exam.
Chanakya Edge
- Strong foundation in computer science, mathematics, machine learning, optimization, and artificial intelligence.
- Specialized pathways in Artificial Intelligence & Machine Learning, Data Analytics & Business Intelligence, and Cloud Computing & Data Engineering.
- Curriculum includes emerging domains such as Responsible AI, Generative AI, Natural Language Processing, Reinforcement Learning, Computer Vision, Edge Computing, and AI-powered Cybersecurity.
- Limited memory-based assessments, with evaluation focused on model development, algorithm design, data-driven problem solving, and AI-centric capstone projects.
- Hands-on experience with modern AI workflows including data preparation, model training, deployment, monitoring, and AI systems engineering.
- Exposure to industry-relevant practices such as MLOps, LLMOps, cloud computing, and scalable AI infrastructure.
- Opportunities to work on interdisciplinary projects spanning healthcare, geoinformatics, education, mobility, cybersecurity, and intelligent automation.
- Active engagement with the AI Focus Group, enabling students to participate in the development of Small Language Models (SLMs), applied AI research, and emerging AI applications.
Programme Highlights
- PEO1: Graduates will establish themselves as competent professionals in computer science, artificial intelligence, data systems and intelligent software engineering.
- PEO2: Graduates will apply computing and AI principles to solve complex engineering, business and societal problems with attention to ethics, fairness, privacy, security and sustainability.
- PEO3: Graduates will pursue higher education, research, entrepreneurship, certifications and lifelong learning in emerging areas such as GenAI, responsible AI, cloud AI systems, NLP and autonomous systems.
- PEO4: Graduates will demonstrate leadership, communication and teamwork while contributing to interdisciplinary and AI-enabled technology projects.
Programme Structure
- Year 1: Foundation in engineering sciences, mathematics, and ethics.
- Year 2: Domain-focused courses with integrated lab work.
- Year 3: Industry internships, electives in emerging tech, and innovation projects.
- Year 4: Capstone projects, entrepreneurial incubation, national & global exposure
Sno | Category | Suggested break up of credits |
1 | Humanities and social sciences including management courses | 12 |
2 | Basic science courses + Mathematics | 18 |
3 | Engineering science courses first year | 9 |
4 | Professional core | 48 |
5 | Professional elective | 24 |
6 | Open electives | 17 |
7 | Project work | 12 |
8 | Non Credit Mandatory courses | Non-credit |
Total | 140 | |
Course Name |
Computer Organisation and Design |
Data Structures and Algorithms |
Database Management Systems |
Mathematical Optimisation |
Introduction to AI & ML |
Foundations of Machine Learning |
Deep Learning |
Natural Language Processing |
Generative AI |
Design Thinking with AI |
Computer Vision |
AI ML Systems |
Responsible AI |
Data Analytics |
Collaborations
Industry collaborations provide students with opportunities for internships, project mentorship, workshops, and exposure to real-world AI applications and deployment environments.
- Zoho
- Tech Mahindra
- Pure Storage
- FalconFeeds.io
- Growteq
- Industry partners across Artificial Intelligence, Data Science, Cloud Computing, and Digital Technologies