Master of Science (M.Sc.) Artificial Intelligence Course, Eligibility, Fee, curriculum | Bennett University

Master of Science (M.Sc.) Artificial Intelligence Course, Eligibility, Fee, curriculum | Bennett University
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M.Sc. Artificial Intelligence
M.Sc.
( Artificial Intelligence )
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M.Sc.
( Artificial Intelligence )
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About
M.Sc. (Artificial Intelligence)
The Master of Science (M.Sc.) Artificial Intelligence at Bennett University is a 2-year postgraduate program crafted for students aspiring to become AI scientists, researchers, and advanced domain experts. This program deepens scientific and mathematical understanding of AI and emphasises research, innovation, and specialisation in next-generation AI.
Students gain expertise in areas such as Advanced Deep Learning, Reinforcement Learning, and Generative AI, culminating in a 20-credit Academic/Industry Project with Dissertation. The program is aligned with NEP 2020 and the National Credit Framework (NCrF), preparing graduates for high-impact roles in R&D, AI innovation, and Ph.D. pathways globally.
Duration
2 Years
Specialisations
4
Credits
80
Start Date
July 2026
Eligibility Criteria
Indian National:
Bachelor’s degree in Engineering/Technology or equivalent of BCA/B.Sc with minimum 60% marks.
Selection Process
Selection will be based on Aggregate Graduation Percentage.
M.Sc. (Artificial Intelligence) Program Fee
Get detailed information about the M.Sc. Artificial Intelligence program fee structure, including tuition, hostel fee, and other applicable charges.
Click here for details.
Why M.Sc. (Artificial Intelligence) from Bennett University
Our M.Sc. (AI) program is designed for students who aspire to pursue advanced research, scientific inquiry, and innovation in Artificial Intelligence.
Research Focus:
Emphasises academic and industry projects with a 20-credit dissertation, building strong research and analytical capabilities.
Advanced Core Curriculum:
Covers Advanced Deep Learning, Reinforcement Learning, and Physics-Inspired Machine Learning, providing depth in algorithmic and mathematical understanding.
Scientific Foundation:
Develops strong foundations in mathematical modeling, algorithm design, and scientific problem-solving, preparing students for Ph.D. programs and high-level R&D roles.
Specialisation Tracks:
Offers domain-specific electives in Signal Processing, Robotics, Computer Vision, and Natural Language Processing (NLP).
NEP 2020 Aligned:
The curriculum follows NEP 2020 and National Credit Framework (NCrF), ensuring flexibility, credit recognition, and alignment with global standards.
Specialisations
Students choose specialised elective tracks to gain expertise in high-demand research and industrial areas.
Signal Processing:
Students gain expertise in analysing and manipulating signals for AI applications, with courses like Adaptive Signal Processing and AI for Bio-Medical Signal Processing.
Robotics:
Focuses on autonomous systems, decision-making, and deep learning integration, with elective courses such as Bio-Inspired Robotics and Probability Robotics.
Computer Vision:
Centres on visual intelligence applications, covering core techniques and advanced topics like Deep Learning for Computer Vision and 3D Computer Vision.
Natural Language Processing (NLP):
Specialises in language and text generation, covering Foundations of LLMs and Advanced NLU Techniques to build sophisticated NLP systems.
Program Objectives
To provide a rigorous scientific and mathematical foundation for students seeking to deepen their understanding of AI. The program aims to train future AI scientists, researchers, and specialised domain experts, preparing them for high-level research and development roles through advanced coursework and a substantial research dissertation.
Learning Outcomes
Students will gain mastery in complex mathematical modeling and advanced algorithm design (e.g., Deep Learning and Reinforcement Learning). They will possess specialised domain knowledge in areas like Generative AI, Computer Vision, and Robotics, along with scientific writing skills, positioning them for Ph.D. programs and roles requiring deep scientific knowledge.
Curriculum Details
The M.Sc. (AI) curriculum is structured to balance theoretical rigour with research application, ensuring graduates are prepared for both academia and industry.
Core Subjects:
Mathematics for Computing, Advanced Machine Learning, Advanced Deep Learning, Natural Language Processing & Generative AI, Reinforcement Learning, Research Methodology & Ethics in AI, etc.
Specialisations:
Signal Processing, Robotics, Computer Vision, Natural Language Processing (NLP).
Minors:
Not applicable. Specialisation is achieved through electives.
Research Component:
20-credit Academic/Industry Project with Dissertation. The 2-Year M.Sc. track requires 40 Research / Dissertation Credits.
Career Pathways
Graduates of the M.Sc. Artificial Intelligence programs are prepared for careers in research, development, and advanced AI applications across academia, industry, and government sectors. Career opportunities include roles such as AI Scientist (R&D Labs), Machine Learning Researcher, Deep Learning Engineer, Quantitative Analyst (FinTech), Domain Specialist in areas like Robotics or Computational Biology, and opportunities to pursue Ph.D. programs globally.
Faculty
Prof. (Dr.) Rajeev Tiwari
Dean, School of Artificial Intelligence
Dr. Manoj Sharma
Ph.D. - IIT Delhi
Dr. Pratyush Pranav
Ph.D. - University of Groningen
Dr. Dinesh Kumar
Ph.D. - University of Coimbra, Portugal
Dr. Shakshi Sharma
Ph.D. - University of Tartu, Estonia
Dr. Neelam Choudhary
Ph.D. - IIT Guwahati
Dr. Navneet Pratap Singh
Ph.D. - IIT Indore
Dr. Yajnaseni Dash
Ph.D. - IIT Delhi
Dr. Gaurav Sharma
Ph.D. - NIT, Hamirpur
Dr. Prateek Yadav
Ph.D.- IIT Delhi
OUR ASK
M.Sc. (Artificial Intelligence) Program
Frequently Asked Questions
What is the main difference between M.Sc AI and M.Tech AI?
The M.Sc. AI focuses on AI theory, mathematics, and research (20-credit dissertation), while the M.Tech. emphasises engineering, system development, and industrial AI applications.
What are the key skills I will gain with M.Sc. in Artificial Intelligence?
You will gain mastery in complex mathematical modeling, advanced algorithm design (e.g., Deep Learning and Reinforcement Learning), scientific writing, and specialised domain knowledge in areas like Generative AI, Computer Vision, and Robotics.
What are the eligibility criteria for the M.Sc. (AI) 2-year track?
Candidates require a 3-Year UG Degree (Generic/Professional) in a relevant discipline (e.g., B.Sc., BCA, B.Tech.). Selection is based on the academic merit of the UG degree, a written test, and/or a personal interview.
What is the duration and total credit requirement for M.Sc. AI?
The M.Sc. Artificial Intelligence program is a 2-Year program for those with a 3-year UG entry, with a total credit requirement of 80 credits, aligned with the NCrF.
What is the focus of the M.Sc. (AI) Program?
The program is a rigorous postgraduate degree designed for students seeking to deepen their scientific and mathematical understanding of AI, leading to high-level research and specialised development roles, with a substantial research dissertation.
What specialisation tracks are Available?
Students can choose specialised elective tracks in Signal Processing, Robotics, Computer Vision, and Natural Language Processing (NLP) to gain expertise in high-demand research areas.
What are some examples of core courses?
Some of the core courses include Mathematics for Computing, Advanced Machine Learning, Advanced Deep Learning, Natural Language Processing & Generative AI, Reinforcement Learning, etc.
What is the Dissertation Requirement?
The program requires a major Academic/Industry Project with Dissertation (20 Credits), completed over Semesters III and IV, to solve complex, novel problems in a chosen specialisation domain.
Does the program include global exposure?
Yes, through international collaborations, students gain insights into global AI research practices and academic exchanges.
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