
Program Details
Master methods for building intelligent systems using engineering principles, mathematics, and computational techniques.
Apply machine learning, optimisation, and algorithmic fairness techniques to support data driven decision making.
Use generative models and probabilistic reasoning for complex analytical tasks.
Translate theoretical foundations of machine learning into applied solutions for real-world problems.
Communicate technical outcomes through structured reports and academic or professional presentations.
Dual-Domain Structure: Our curriculum covers Artificial Intelligence and Decision Science as distinct yet connected areas, each supported by a dedicated capstone project.
Laboratory-Intensive Learning: Practical laboratory sessions help you apply theoretical concepts to computational tasks and data-driven problems.
Industry-Aligned Curriculum: Industry professionals contribute to curriculum planning, supporting strong links with internships, training, and placement opportunities.
Industry-Sponsored Projects: You work on real-time problems proposed by industry partners, gaining exposure to professional work environments.
Research-Linked Engagement: Faculty-led research projects provide opportunities to present or publish work in recognised conferences and journals.

Advanced Exposure
Work in high-performance computing environments that support routine coursework and research-based assignments in machine intelligence and decision science.
Advanced Exposure
Work in high-performance computing environments that support routine coursework and research-based assignments in machine intelligence and decision science.
Our meaningful collaborations with industry leaders and global universities enhance your career opportunities and higher education pathways.
Placements








Admissions Path
Merit List
Merit list based on MET score or PGCUET score.
Admission Criteria
Provisional Admission is offered to shortlisted candidates based on merit and vacancy.
Program Eligibility
You must successfully complete BE/BTech in the relevant branch from a recognized university with a minimum of 50% aggregate marks.
You must either appear for the Manipal Entrance Test (MET)/ PGCUET 2026 and get the required cut off.
*PGCUET scores will be considered only for:
Curriculum and Fees
Core subjects include Mathematical Foundations of Computational Intelligence, Generative AI, Machine Learning theory, Probabilistic Graphical Models, and Fairness in ML, along with Optimization, Data Mining, electives, labs, and capstone projects.
Program Duration
Total Program Fee
General:
Total Program Fee
NRI/Foreign:
Scholarships
Manipal Academy of Higher Education (MAHE) Manipal awards scholarship for its Under Graduate (UG) and Post Graduate (PG) students under the following categories


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