B.Tech. – CSE (Artificial Intelligence & Machine Learning) – with Curriculum Aligned to NVIDIA Deep Learning Institute (DLI)

Duration4 Years

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Course Fee12.90 Lakhs

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Eligibility10+2 *

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B.Tech. – CSE (Artificial Intelligence & Machine Learning) – with Curriculum Aligned to NVIDIA Deep Learning Institute (DLI)

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.

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Programme Outcomes (PO)

To apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.

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To identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.

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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.

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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.

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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.

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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.

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To understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.

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To apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.

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To function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.

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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.

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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.

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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.

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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
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Career Path

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.

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