Master of Computer Applications (MCA) Artificial Intelligence Courses, Syllabus,Fees | Bennett University
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Master of Computer Applications (MCA) Artificial Intelligence Courses, Syllabus,Fees | Bennett University
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Master of Computer Application – Artificial Intelligence
Master of Computer Applications
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Master of Computer Applications
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About M.Tech Artificial Intelligence & Machine Learning
About
MCA (Artificial Intelligence)
The Master of Computer Applications (Artificial Intelligence) at Bennett University is a 2-year postgraduate program designed to equip students with advanced technical and analytical expertise in AI and software development. The curriculum integrates core subjects such as Machine Learning, Agentic AI, Data Analytics, and Intelligent Systems, aligned with NEP 2020 and NCrF guidelines. With a strong focus on experiential learning, it includes a 20-credit Academic/Industry Project and Dissertation to build hands-on, industry-ready skills.
Graduates are prepared for high-demand roles like AI Developer, Machine Learning Engineer, Data Scientist, and AI Architect across global technology sectors.
Duration
2 Years
Specializations
4
Credits
80
Start Date
July 2026
Eligibility Criteria
Indian National:
Passed Graduation in any discipline from recognised university with minimum 50% marks with English as medium of instruction.
NRI:
The eligibility criterion for all programs for international applicants is minimum 50% in the qualifying examination and having studied the pre-requisite subjects for admission in to the desired program.
Selection Process
Selection will be based on Aggregate Graduation Percentage.
MCA (Artificial Intelligence) Program Fee
Get detailed information about the MCA (Artificial Intelligence) program fee, including tuition, hostel charges, and other applicable costs.
Click here for details.
Why Choose MCA (Artificial Intelligence) from Bennett University
Our MCA (AI) program is designed to bridge the gap between core computer science knowledge and modern
AI applications, enabling students to deploy intelligent systems at scale.
Applied AI & Development:
Focus on developing and deploying scalable,
AI-powered software applications.
Advanced Programming Core:
Rigorous training in Advanced Machine Learning,
Agentic AI, and Data/Signal Analytics.
Industry-Ready Capstone:
20-credit Academic/Industry Project with Dissertation
for real-world problem-solving.
NEP 2020 & NCrF Aligned:
Flexible structure ensuring outcome-based learning
and skill-based progression.
Application-Focused:
Emphasises development and deployment of AI systems rather
than only theoretical research.
Specialisations
Students can choose specialised electives to develop deep expertise in key AI
domains:
Signal Processing:
Learn Adaptive Signal Processing, AI for Biomedical Signals,
and Statistical Signal Processing.
Robotics:
Explore Deep Learning for Robotics, Bio-Inspired Robotics, and
Probabilistic Robotics.
Computer Vision:
Gain skills in Deep Learning for Computer Vision, Augmented
and Virtual Reality (AR/VR).
Natural Language Processing (NLP):
Master systems for language understanding with
courses like Foundation of Large Language Models and Advanced NLU Techniques.
Program Objectives
The program aims to prepare graduates with practical AI development skills, advanced programming
expertise, and strong theoretical grounding to design, develop, and deploy complex AI systems. It
transforms learners into industry-ready professionals capable of managing end-to-end AI projects
across domains such as Fintech, Robotics, Computer Vision, and Natural Language Processing.
Learning Outcomes
Upon completing the MCA in AI, students will be able to:
Develop End-to-End AI Systems:
Implement scalable and efficient AI-driven
software solutions.
Master Advanced Programming Tools:
Gain proficiency in Agentic AI, Advanced ML,
and Deep Learning.
Integrate AI in Real-World Systems:
Build intelligent systems adaptable to
business and research environments.
Lead AI Projects:
Manage multidisciplinary teams and oversee AI project
lifecycles.
Conduct Applied Research:
Undertake real-world projects and dissertations integrating
scientific and business insights.
Curriculum Details
The curriculum is designed with a meticulous and interdisciplinary approach with a focus on core
subjects. By taking a holistic approach, it covers not only the technical aspects but also the
practical applications of AI technologies.
Core Subjects:
Mathematics for Computing, Advanced Machine Learning, Advanced Deep
Learning, Agentic AI, AI Tools & Applications, Image Processing & Computer Vision, Natural
Language Processing & Generative AI.
Majors:
Master of Computer Applications (MCA) in Artificial Intelligence, emphasising
applied learning, coding, and domain specialisation.
Minors:
Not applicable for this program.
Electives:
Offered in Signal Processing, Robotics, Computer Vision, and NLP.
Research/Dissertation Component:
Mandatory 20-credit Capstone Project/Dissertation,
contributing to 40 Research/Dissertation Credits out of 80 total.
Career Pathways
Graduates of the MCA (Artificial Intelligence) program are highly sought after in both technical and
managerial roles across industries, leveraging AI technologies. Career Opportunities include AI
Developer / Architect, Machine Learning Engineer, Data Scientist / Analyst, AI Solutions Consultant,
Software Development Manager (AI Specialisation).
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
MCA (Artificial Intelligence)
Frequently Asked Questions
What is the primary focus of the MCA (AI) program?
The MCA (AI) program is application-oriented, focusing on the implementation, development, and deployment of AI systems using advanced programming, software engineering, and data management practices.
What is the difference between MCA (AI) and M.Tech AI?
MCA (AI) focuses on the software and application layer of AI—coding, design, and deployment, while M.Tech AI emphasises engineering, algorithms, and hardware optimisation.
Is a background in Mathematics mandatory?
Yes, candidates should have Mathematics at the 10+2 or undergraduate level to handle algorithmic and analytical coursework.
What is the Capstone/Dissertation requirement?
Students must complete a 20-credit Academic/Industry Project with a Dissertation, developing a complex, end-to-end AI application.
What specialisation tracks are available?
Students can specialise in Signal Processing, Robotics, Computer Vision, or NLP through dedicated electives.
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Master of Computer Application – Artificial Intelligence
Master of Computer Applications
(Artificial Intelligence)
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Master of Computer Applications
(Artificial Intelligence)
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About M.Tech Artificial Intelligence & Machine Learning
About
MCA (Artificial Intelligence)
The Master of Computer Applications (Artificial Intelligence) at Bennett University is a 2-year postgraduate program designed to equip students with advanced technical and analytical expertise in AI and software development. The curriculum integrates core subjects such as Machine Learning, Agentic AI, Data Analytics, and Intelligent Systems, aligned with NEP 2020 and NCrF guidelines. With a strong focus on experiential learning, it includes a 20-credit Academic/Industry Project and Dissertation to build hands-on, industry-ready skills.
Graduates are prepared for high-demand roles like AI Developer, Machine Learning Engineer, Data Scientist, and AI Architect across global technology sectors.
Duration
2 Years
Specializations
4
Credits
80
Start Date
July 2026
Eligibility Criteria
Indian National:
Passed Graduation in any discipline from recognised university with minimum 50% marks with English as medium of instruction.
NRI:
The eligibility criterion for all programs for international applicants is minimum 50% in the qualifying examination and having studied the pre-requisite subjects for admission in to the desired program.
Selection Process
Selection will be based on Aggregate Graduation Percentage.
MCA (Artificial Intelligence) Program Fee
Get detailed information about the MCA (Artificial Intelligence) program fee, including tuition, hostel charges, and other applicable costs.
Click here for details.
Why Choose MCA (Artificial Intelligence) from Bennett University
Our MCA (AI) program is designed to bridge the gap between core computer science knowledge and modern
AI applications, enabling students to deploy intelligent systems at scale.
Applied AI & Development:
Focus on developing and deploying scalable,
AI-powered software applications.
Advanced Programming Core:
Rigorous training in Advanced Machine Learning,
Agentic AI, and Data/Signal Analytics.
Industry-Ready Capstone:
20-credit Academic/Industry Project with Dissertation
for real-world problem-solving.
NEP 2020 & NCrF Aligned:
Flexible structure ensuring outcome-based learning
and skill-based progression.
Application-Focused:
Emphasises development and deployment of AI systems rather
than only theoretical research.
Specialisations
Students can choose specialised electives to develop deep expertise in key AI
domains:
Signal Processing:
Learn Adaptive Signal Processing, AI for Biomedical Signals,
and Statistical Signal Processing.
Robotics:
Explore Deep Learning for Robotics, Bio-Inspired Robotics, and
Probabilistic Robotics.
Computer Vision:
Gain skills in Deep Learning for Computer Vision, Augmented
and Virtual Reality (AR/VR).
Natural Language Processing (NLP):
Master systems for language understanding with
courses like Foundation of Large Language Models and Advanced NLU Techniques.
Program Objectives
The program aims to prepare graduates with practical AI development skills, advanced programming
expertise, and strong theoretical grounding to design, develop, and deploy complex AI systems. It
transforms learners into industry-ready professionals capable of managing end-to-end AI projects
across domains such as Fintech, Robotics, Computer Vision, and Natural Language Processing.
Learning Outcomes
Upon completing the MCA in AI, students will be able to:
Develop End-to-End AI Systems:
Implement scalable and efficient AI-driven
software solutions.
Master Advanced Programming Tools:
Gain proficiency in Agentic AI, Advanced ML,
and Deep Learning.
Integrate AI in Real-World Systems:
Build intelligent systems adaptable to
business and research environments.
Lead AI Projects:
Manage multidisciplinary teams and oversee AI project
lifecycles.
Conduct Applied Research:
Undertake real-world projects and dissertations integrating
scientific and business insights.
Curriculum Details
The curriculum is designed with a meticulous and interdisciplinary approach with a focus on core
subjects. By taking a holistic approach, it covers not only the technical aspects but also the
practical applications of AI technologies.
Core Subjects:
Mathematics for Computing, Advanced Machine Learning, Advanced Deep
Learning, Agentic AI, AI Tools & Applications, Image Processing & Computer Vision, Natural
Language Processing & Generative AI.
Majors:
Master of Computer Applications (MCA) in Artificial Intelligence, emphasising
applied learning, coding, and domain specialisation.
Minors:
Not applicable for this program.
Electives:
Offered in Signal Processing, Robotics, Computer Vision, and NLP.
Research/Dissertation Component:
Mandatory 20-credit Capstone Project/Dissertation,
contributing to 40 Research/Dissertation Credits out of 80 total.
Career Pathways
Graduates of the MCA (Artificial Intelligence) program are highly sought after in both technical and
managerial roles across industries, leveraging AI technologies. Career Opportunities include AI
Developer / Architect, Machine Learning Engineer, Data Scientist / Analyst, AI Solutions Consultant,
Software Development Manager (AI Specialisation).
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
MCA (Artificial Intelligence)
Frequently Asked Questions
What is the primary focus of the MCA (AI) program?
The MCA (AI) program is application-oriented, focusing on the implementation, development, and deployment of AI systems using advanced programming, software engineering, and data management practices.
What is the difference between MCA (AI) and M.Tech AI?
MCA (AI) focuses on the software and application layer of AI—coding, design, and deployment, while M.Tech AI emphasises engineering, algorithms, and hardware optimisation.
Is a background in Mathematics mandatory?
Yes, candidates should have Mathematics at the 10+2 or undergraduate level to handle algorithmic and analytical coursework.
What is the Capstone/Dissertation requirement?
Students must complete a 20-credit Academic/Industry Project with a Dissertation, developing a complex, end-to-end AI application.
What specialisation tracks are available?
Students can specialise in Signal Processing, Robotics, Computer Vision, or NLP through dedicated electives.
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