M. Tech. Computer Science and Engineering (Artificial Intelligence)
Duration
Eligibility
Passed Bachelor’s Degree or equivalent. Obtained at least 50% marks (45% marks in case of candidates belonging to reserved category) in the qualifying examination, in relevant branch of Engineering and Technology as mentioned in the AICTE APH 2024-25.
Programme Overview
The Master of Technology in Computer Science and Engineering (Specialization in Artificial Intelligence) is a postgraduate program that explores AI algorithms and their applications across sectors like healthcare, finance, agriculture, retail, and more. The program emphasizes emerging trends in AI and machine learning (ML) and aims to address research challenges while bridging the gap between theory and practice. It focuses on developing real-world applications to solve practical problems. Gartner highlights the growing demand for AI/ML skills, influencing company infrastructure decisions. Career opportunities include roles such as AI/ML engineer, big data analyst, NLP engineer, UX designer, AI researcher, and robotics/computer vision engineer.
Course Curriculum
- 01Advanced Engineering Mathematics - MAT6001
- 02English for Employability - ENG5001
- 03Artificial Intelligence - CSE5005
- 04Knowledge Engineering and Expert Systems - CSE5006
- 05Machine Learning Algorithms - CSE5007
- 06Discipline Elective – I - CSEXXXX
- 07Discipline Elective – II - CSEXXXX
- 08Seminar – I - SEM5001
- 01Deep Learning - CSE6001
- 02Natural Language Processing Techniques - CSE6002
- 03Discipline Elective – III - CSEXXXX
- 04Discipline Elective – IV - CSEXXXX
- 05Discipline Elective – V - CSEXXXX
- 06Open Elective – I - XXXXXXX
- 07Open Elective – II - XXXXXXX
- 08Seminar – II - SEM5002
- 01Dissertation/Internship – I - PIP6001
- 01Dissertation/Internship – II - PIP6002
Programme Educational Objectives
After two years of successful completion of the program, the graduates shall be:
PEO 01: To prepare graduates who will be successful professionals in industry, government, academia, research, entrepreneurial pursuit and consulting firms.
PEO 02: To prepare graduates who will contribute to society as broadly educated, expressive, ethical and responsible citizens with proven expertise.
PEO 03: To prepare graduates who will achieve peer recognition as individuals or in a team through demonstration of good analytical, research, design and implementation skills.
PEO 04: To prepare graduates who will thrive to pursue life-long reflective learning to fulfil their goals.
Programme Outcomes (POs)
On successful completion of the Program, the students shall be able to:
PO 1: An ability to analysis, manage and supervise engineering systems and processes with the aid of appropriate advanced tools.
PO 2: An ability to design a system and process within constraints of health, safety, security, economics, manufacturability to meet desired needs.
PO 3: An ability to carry out research in the respective discipline and publish the findings.
PO 4: An ability to effectively communicate and transfer the knowledge/ skill to stakeholders.
PO 5: An ability to realize the impact of engineering solutions in a contemporary, global, economic, environmental, and societal context for sustainable development.
Programme Specific Outcomes
Upon completion of the M. Tech. in AI program, students shall be able to:
PSO 01: [Problem Analysis]: Identify, formulate, research literature, and analyze complex engineering problems related to AI and ML principles and practices, Programming and Computing technologies reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
PSO 02: [Design/development of Solutions]: Design solutions for complex engineering problems related to AI and ML principles and practices, Programming and Computing technologies and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, cultural, societal and environmental considerations.
PSO 03: [Modern Tool usage]: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities related to AI & ML principles and practices, Programming AI and ML Computing & analytics with an understanding of the limitations.
Student handbook
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USP
- Advanced Curriculum covering deep learning, neural networks, NLP, computer vision, and AI-driven automation.
- State-of-the-Art AI Labs equipped with high-performance GPUs, TensorFlow, PyTorch, and cloud AI platforms.
- Expert Faculty & Research Mentorship providing hands-on experience in cutting-edge AI applications.
- Research & Innovation Focus with opportunities to work on AI-based research projects, publications, and patents.
- Global Internship & Study Abroad Opportunities for international exposure and career advancement.
- Strong Industry Collaborations with AI-driven tech firms for internships, research projects, and industry certifications.
- AI & ML Certifications including TensorFlow, AWS AI, and Microsoft Azure AI to enhance employability.
- Excellent Placement Records with top recruiters in AI research, automation, data science, and AI-driven industries.
Career Opportunities
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Core AI Roles
Machine Learning Engineer: Design and implement machine learning models for tasks like recommendation systems, fraud detection, and predictive analytics.
Data Scientist: Analyze and interpret complex datasets to uncover trends and insights.
AI Research Scientist: Conduct research in AI subfields like deep learning, reinforcement learning, and natural language processing.
Computer Vision Engineer: Develop algorithms for image and video analysis, such as facial recognition and autonomous vehicles.
Natural Language Processing (NLP) Specialist: Focus on language-related AI applications, like chatbots, translation systems, and sentiment analysis.
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AI in Industry-Specific Roles
Robotics Engineer: Work on AI-driven robots for automation in manufacturing, healthcare, or logistics.
AI Product Manager: Oversee the development and deployment of AI-powered products.
Autonomous Systems Engineer: Design AI for self-driving cars, drones, or delivery bots.
Healthcare AI Specialist: Develop diagnostic tools, treatment planning systems, and health monitoring devices using AI.
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Cross-Disciplinary and Emerging Roles
AI Ethics Consultant: Address ethical concerns and biases in AI systems while ensuring compliance with global regulations such as GDPR.
AI Consultant: Provide AI-driven solutions to organizations for process optimization and innovation.
Edge AI Developer: Focus on deploying AI models on edge devices like IoT gadgets for real-time processing.
AI-Blockchain Developer: Work on integrating AI with blockchain for secure data-driven applications.
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Academic and Research Roles
AI Lecturer/Professor: Teach AI and machine learning courses in academic institutions.
Research Scientist: Work with ISRO, DRDO, or CSIR on AI-related research projects.
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Entrepreneurship
AI Startup Founder: Launch startups focused on innovative AI applications in fields like fintech, edtech, or Healthtech.