This two-year postgraduate program in Master of Computer Applications with a specialization in Artificial Intelligence (AI) and Machine Learning (ML) builds on undergraduate computing knowledge to deliver advanced expertise in intelligent computing technologies.
Programme at a Glance
About the Programme
This two-year postgraduate program in Master of Computer Applications with a specialization in Artificial Intelligence (AI) and Machine Learning (ML) builds on undergraduate computing knowledge to deliver advanced expertise in intelligent computing technologies. It integrates core MCA subjects such as advanced programming, database systems, software engineering, cloud computing, and big data with specialized training in AI/ML domains including machine learning algorithms, deep learning architectures, neural networks, natural language processing (NLP), computer vision, reinforcement learning, generative AI, explainable AI, ethical/responsible AI, model deployment (MLOps), and data analytics.
Through a blend of theoretical foundations, hands-on labs using tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud platforms, industry-oriented projects, research components, internships, and capstone work, students develop the ability to design, train, evaluate, deploy, and optimize AI-powered intelligent systems for real-world applications in healthcare (e.g., predictive diagnostics), finance (fraud detection), autonomous technologies, recommendation systems, natural language interfaces, and more—preparing them to lead in the AI-driven innovation economy and address complex, data-centric challenges with scalable, ethical solutions.
Advanced AI & ML Curriculum
Covers machine learning, deep learning, NLP, computer vision, reinforcement learning, generative AI, explainable AI, and ethical AI with practical implementation.
Industry-Focused MCA Specialization
Combines core MCA subjects like advanced programming, database systems, software engineering, cloud computing, and big data with cutting-edge AI/ML technologies.
Hands-On Learning & Tools
Practical labs and projects using Python, TensorFlow, PyTorch, scikit-learn, and leading cloud platforms for real-world AI development.
MLOps & Deployment Expertise
Learn AI model deployment, cloud integration, scalable system design, and MLOps practices for production-ready intelligent solutions.
Data-Driven Problem Solving
Build expertise in predictive analytics, fraud detection, intelligent automation, and scalable AI systems for complex business challenges.
Four core aims that define what this programme sets out to achieve for every student.
01
Expert Knowledge in AI & CS
Build expertise in predictive analytics, fraud detection, intelligent automation, and scalable AI systems for complex business challenges.
02
Ethical AI Professionals
Cultivate professionals skilled in building adaptive, data-driven, and autonomous solutions using state-of-the-art AI/ML techniques.
03
Research, Innovation & Leadership
Prepare graduates for high-impact roles in industry, research, innovation, and leadership in emerging AI technologies.
04
Industry 4.0 / 5.0 Readiness
Align curriculum with global standards, Industry 4.0/5.0 needs, ethical AI practices, and practical tools for sustainable technological advancement.
Four core aims that define what this programme sets out to achieve for every student.
Provide in-depth mastery of core computer applications alongside cutting-edge AI/ML concepts like deep learning, NLP, computer vision, and big data processing.
Train in advanced techniques for model building, training, evaluation, optimization, and production deployment.
Enhance analytical, algorithmic, programming, and critical thinking skills for solving intricate, real-world problems with AI.
Offer extensive practical experience through projects, research, internships, industry collaborations, and hackathons.
Promote innovation, ethical awareness, interdisciplinary collaboration, entrepreneurship, and lifelong learning in the fast-evolving AI landscape.
Equip graduates for global careers, Ph.D./research pursuits, or contributions to AI-driven societal and industrial progress.
Eight measurable outcomes that every graduate of this programme will demonstrate.
PO1
Technical Proficiency
Apply comprehensive knowledge of computer applications, mathematics, statistics, AI, and ML to address complex computational and intelligent problems.
PO2
Problem Analysis & Intelligent Solutions
Apply comprehensive knowledge of computer applications, mathematics, statistics, AI, and ML to address complex computational and intelligent problems.
PO3
System Design & Development
Design, implement, train, evaluate, deploy, and maintain scalable AI/ML models and applications with emphasis on performance, robustness, and ethics.
PO4
Modern Tool Usage
Proficiently utilize advanced tools, languages, and platforms (e.g., Python, TensorFlow, PyTorch, scikit-learn, cloud ML services, MLOps tools) for end-to-end AI development and experimentation.
PO5
Research & Innovation
Conduct research, innovate in AI/ML domains, critically evaluate models, and contribute novel solutions or improvements.
PO6
Teamwork & Communication
Collaborate in multidisciplinary teams, lead AI projects, and effectively communicate technical findings, architectures, and results.
PO7
Ethics & Sustainability
Apply ethical principles, mitigate biases, ensure fairness/privacy/security, and promote sustainable, inclusive AI practices.
PO8
Lifelong Learning
Adapt to emerging AI technologies, methodologies, and trends through continuous professional development and self-learning.
Graduates are equipped for the most exciting and high-growth roles in the AI & tech
industry.
Design and deploy production-grade ML models and intelligent systems at scale.
Extract insights from complex datasets using statistical modelling and advanced analytics.
Conduct cutting-edge research in deep learning, NLP, or CV at top research labs worldwide.
Build language models, translation systems, and intelligent dialogue agents.
Build image recognition, object detection, and video analytics systems for real-world deployment.
Guide enterprises in adopting intelligent systems and AI-driven transformation strategies.
Manage model lifecycle, CI/CD pipelines, and scalable AI infrastructure on cloud platforms.
Work with LLMs, diffusion models, and multimodal AI for creative and enterprise applications.