B.Tech. – CSE (Artificial Intelligence & Machine Learning) – with Curriculum Aligned to NVIDIA Deep Learning Institute (DLI)
Duration4 Years
Course Fee12.90 Lakhs
Eligibility10+2 *
The B.Tech. in Computer Science and Engineering with a specialization in Artificial Intelligence and Machine Learning (AIML) is an advanced, interdisciplinary undergraduate program designed to integrate core computer science fundamentals with the cutting-edge principles of intelligent systems.
The program equips students with extensive knowledge in data structures, algorithms, machine learning models, deep learning, natural language processing, neural networks, and computer vision. It emphasizes strong practical learning, industry-aligned exposure, and research-driven innovation to solve complex real-world data and automation challenges across sectors like healthcare, finance, automotive, and technology.
Program Specialisation
Program Highlights
- NVDIA DLI - Industry-Aligned Curriculum: Designed in alignment with modern automation and data intelligence requirements to meet global technological standards.
- Core AIML Competencies: Focus on the development and application of intelligent agents, deep learning architectures, cognitive computing, and large language models.
- Hands-on Exposure: Practical training on modern engineering tools, computing environments, high-performance GPUs, and cloud AI platforms.
- Emerging Tech Integration: Exposure to high-demand sub-fields such as Generative AI, Predictive Analytics, Computer Vision, and Big Data Technologies.
- Innovation & Research: Opportunities to participate in capstone projects, hackathons, and research initiatives focusing on algorithmic innovations and automation to address complex societal problems.
Program Specific Outcomes (PSO)
PSO 01
Apply algorithmic thinking and vibe coding skills to develop and maintain efficient and robust computing systems.
PSO 02
Solve real-world problems using technology while adapting to AI-driven innovations through collaboration, ethics, and continuous learning.
PSO 03
Develop proficiency in artificial intelligence, and machine learning techniques to design and develop intelligent solutions for real-world problems.
Programme Outcomes (PO)
To apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
To identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
To design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
To use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
To create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities with an understanding of the limitations.
To apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
To understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
To apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
To function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
To communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
To demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
To recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.
Curriculum Details
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| MAT1119 | Matrix Methods and Linear Algebra | 3 | 1 | 0 | 4 |
| COM1531 | Contemporary English | 0 | 0 | 4 | 2 |
| PHY1009/ | Elements of Modern Physics/ Elements of Modern Physics Lab | 3 | 0 | 2 | 4 |
| CSE1010/ CSE1010L | Problem Solving through Programming / Programming and Problem Solving Lab | 3 | 0 | 2 | 4 |
| CSE1711 | Artificial Intelligence and its Real-World Applications | 2 | 0 | 0 | 2 |
| VAC001 | Design Thinking | 2 | 0 | 0 | 2 |
| EGR1501 | Induction Program | 0 | 0 | 0 | 0 |
| Total Credits | 18 | ||||
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| MAT1120/MAT1120L | Probability and Statistics using Python/Probability and Statistics using Python Lab | 3 | 0 | 2 | 4 |
| PHY1008/PHY1008L | Quantum Mechanics/Quantum Mechanics Lab | 3 | 0 | 2 | 4 |
| ELE1001/ELE1001L | Fundamentals of Electrical and Electronics Engineering/ Fundamentals of Electrical and Electronics Engineering Lab | 3 | 0 | 2 | 4 |
| CSE1009/CSE1009L | Data Structures / Data Structures Lab | 3 | 0 | 2 | 4 |
| COM1542 | Professional Skills and Ability Enhancement | 0 | 0 | 4 | 2 |
| LAW1010 | Introduction to the Indian Constitution | 3 | 0 | 0 | 0 |
| VAC004 | Introduction to Indian Knowledge System | 2 | 0 | 0 | 2 |
| MEC1509 | Engineering Graphics | 0 | 0 | 4 | 2 |
| Total Credits | 22 | ||||
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| MAT1123 / MAT1123L | Discrete Mathematical Structures / Discrete Mathematical Structures Lab | 3 | 0 | 2 | 4 |
| CSE2036/CSE2036L | Programming using Java/Programming using Java Lab | 3 | 0 | 2 | 4 |
| ELX2705 | Digital Logic Design | 2 | 0 | 0 | 2 |
| CSE2034/CSE2034L | Introduction to Design and Analysis of Algorithms/ Introduction to Design and Analysis of Algorithms Lab | 3 | 0 | 2 | 4 |
| UHV2701 | Universal Human Values | 3 | 0 | 0 | 3 |
| IDP2501/IDP2502 | Developing a Business Model/Research & Design | 0 | 1 | 4 | 3 |
| Total Credits | 20 | ||||
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| CSE2047/ | Operating Systems: The System that makes computer works Operating Systems: The System that makes computer works Lab | 3 | 0 | 2 | 4 |
| CSE2711 | Computer Organization and Architecture | 3 | 1 | 0 | 4 |
| CSE2037/CSE2037L | Basics of Data Communication and Computer Networks/ Basics of Data Communication and Computer Networks Lab | 3 | 0 | 2 | 4 |
| CSE 2725 | Cognitive Computing & Machine Learning | 3 | 0 | 0 | 3 |
| CSE2732 | AI Ethics and Responsible Computing | 2 | 0 | 0 | 2 |
| IDP2503/IDP2504 | Translating Business Model/ Data Analysis & Publication | 0 | 1 | 4 | 3 |
| ENV1705 | Environmental Studies | 2 | 0 | 0 | 0 |
| Total Credits | 20 | ||||
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| CSE 3050/ CSE3050L | Artificial Neural Network / Artificial Neural Network Lab | 3 | 0 | 2 | 4 |
| CSE3727 | Advanced Artificial Intelligence | 2 | 1 | 0 | 3 |
| ELX3008/ELX3008L | Microprocessor and Microcontrollers/ Microprocessor and Microcontrollers Lab | 3 | 0 | 2 | 4 |
| Professional Elective-I | 3 | 1 | 0 | 4 | |
| CSE3022/ | DBMS with NoSQL/DBMS with NoSQL Lab | 3 | 0 | 2 | 4 |
| CSE3723 | Cloud Computing | 3 | 0 | 0 | 3 |
| Total Credits | 22 | ||||
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| CSE 3045/CSE3045L | Deep Learning/Deep Learning Lab | 3 | 0 | 2 | 4 |
| Professional Elective-II | 3 | 1 | 0 | 4 | |
| Professional Elective-III | 3 | 0 | 2 | 4 | |
| Open Elective-I | 3 | 0 | 0 | 3 | |
| CSE3052/ | Applied Cryptography and Network Security/ Applied Cryptography and Network Security Lab | 3 | 0 | 2 | 4 |
| CSE 3059/CSE3059L | Generative AI / Generative AI Lab | 2 | 0 | 2 | 3 |
| Total Credits | 22 | ||||
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| CSE4045/ | RAG (Retrieval - Augmented Generation) Systems / RAG (Retrieval - Augmented Generation) Systems Lab | 3 | 0 | 2 | 4 |
| CSE2501 | Internet of Things | 3 | 0 | 0 | 3 |
| Professional Elective-IV | 3 | 0 | 2 | 4 | |
| Open Elective II | 3 | 0 | 0 | 3 | |
| EGR4501 | Project Phase-I | 0 | 0 | 0 | 4 |
| EGR4502 | Summer Internship | 0 | 0 | 0 | 2 |
| Total Credits | 20 | ||||
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| Professional Elective-V | 3 | 0 | 2 | 4 | |
| Open Elective III | 3 | 0 | 0 | 3 | |
| EGR4503 | Project Phase-II | 0 | 0 | 0 | 10 |
| Total Credits | 17 | ||||
Career Path

Graduates of the B.Tech. AIML program from SoES possesses a highly sought-after technical skillset, paving the way for lucrative global careers. Prospective job pathways include:
- AI Engineer / Machine Learning Engineer – Designing, building, and deploying production-ready machine learning models and intelligent software.
- Data Scientist / Data Analyst – Interpreting complex datasets, implementing statistical techniques, and turning raw data into actionable insights for business intelligence.
- Deep Learning Specialist – Working on neural network architectures, image recognition, natural language processing, and computer vision systems.
- Automation & Robotics Engineer – Creating automated workflows, software bots, and intelligent hardware systems.
- AI Research Scientist – Advancing foundational AI research, designing new algorithms, and collaborating with global R&D institutions.
- M.Tech / Ph.D. Pathways – Pursuing higher education and specialization fields at elite national and international academic universities.
Fee Structure
Yearly
| 1st Year | 2nd Year | 3rd Year | 4th Year |
|---|---|---|---|
| ₹3,60,000 | ₹3,10,000 | ₹3,10,000 | ₹3,10,000 |
Semester Wise
| 1st Sem | 2nd Sem | 3rd Sem | 4th Sem | 5th Sem | 6th Sem | 7th Sem | 8th Sem |
|---|---|---|---|---|---|---|---|
| ₹2,05,000 | ₹1,55,000 | ₹1,55,000 | ₹1,55,000 | ₹1,55,000 | ₹1,55,000 | ₹1,55,000 | ₹1,55,000 |
Admission Requirement
Passed 10+2 examination with Physics and Mathematics as compulsory course and any one course from Chemistry/ Computer Science/Electronics/Information Technology/ Biology/Informatics Practices/ Biotechnology/ Technical Vocational subject/ Agriculture/ Engineering Graphics/ Business Studies/Entrepreneurship.
Obtained at least 50% marks in the above 2 compulsory and any one selected (from list of 12) subjects taken together.
Passed min. 3 years Diploma examination with at least 50% marks
Eligibility Criteria
Candidates must have passed the 10+2 examination or an equivalent examination from a recognized board with Physics and Mathematics as compulsory subjects along with one of the following: Chemistry / Computer Science / Information Technology.
Obtained at least 50% marks in the above three subjects taken together.
The candidate must have passed English as a subject in the 10+2 examination.
Entrance Examination & Selection Preferability
Preference is given to merit scores achieved in national or state-level tests: IIT-JEE (Mains), State Level Entrance Exams, or the University Entrance Exam (Q-CARE).
In addition, candidates must successfully complete the Goenka Aptitude Test for Admission (GATA) and clear a Personal Interview (PI) round.