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M-TECH in Artificial Intelligence at Vellore Institute of Technology
Vellore, Tamil Nadu
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About the Specialization
What is Artificial Intelligence at Vellore Institute of Technology Vellore?
This M.Tech Artificial Intelligence program at Vellore Institute of Technology focuses on equipping students with advanced theoretical knowledge and practical skills in AI. It covers core areas like Machine Learning, Deep Learning, NLP, and Computer Vision, aligned with India''''s burgeoning tech industry. The curriculum is designed to foster innovation and address complex real-world challenges.
Who Should Apply?
This program is ideal for engineering graduates from various disciplines, MCA degree holders, or M.Sc. in relevant fields looking to specialize in AI. It caters to fresh graduates aiming for cutting-edge roles and working professionals seeking to upskill in advanced AI technologies, enabling them to contribute to India''''s digital transformation.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding careers as AI Engineers, Data Scientists, Machine Learning Specialists, or Research Scientists in top Indian and multinational companies. Starting salaries typically range from INR 6-12 LPA for freshers, with significant growth potential. The program also prepares students for advanced research or entrepreneurial ventures in the AI domain.
Student Success Practices
Foundation Stage
Master Mathematical and Programming Fundamentals- (Semester 1-2)
Dedicate significant time to thoroughly grasp linear algebra, calculus, probability, and statistics, which are bedrock for AI. Simultaneously, strengthen Python programming skills and implement basic algorithms and data structures using platforms like HackerRank or LeetCode.
Tools & Resources
Khan Academy, Coursera (Mathematics for ML), HackerRank, LeetCode, Jupyter Notebook
Career Connection
A strong foundation ensures efficient problem-solving in AI, making you attractive to companies seeking robust analytical and coding skills for AI/ML roles.
Engage in Hands-on ML/DL Projects Early- (Semester 1-2)
Beyond coursework, identify small, manageable AI projects. Start with supervised learning tasks (e.g., classification on Kaggle datasets) and gradually explore deep learning frameworks. Document your projects on GitHub for a visible portfolio.
Tools & Resources
Kaggle, GitHub, Google Colab, PyTorch, TensorFlow
Career Connection
Practical experience on real-world datasets demonstrates your ability to apply theoretical knowledge, crucial for internships and entry-level AI/ML engineering positions.
Participate in AI/Data Science Workshops and Bootcamps- (Semester 1-2)
Actively seek out and attend workshops, webinars, and bootcamps offered by VIT, industry experts, or online platforms. These provide exposure to new tools, techniques, and networking opportunities within the Indian AI community.
Tools & Resources
VIT''''s internal workshops, NPTEL, Online industry events, Meetup groups
Career Connection
Staying updated with industry trends and building a network helps in identifying job opportunities and mentorship, accelerating your career growth in the fast-paced Indian tech sector.
Intermediate Stage
Specialize through Electives and Advanced Courses- (Semester 3)
Strategically choose electives that align with your career interests (e.g., NLP, Computer Vision, Reinforcement Learning). Deep dive into these areas through advanced online courses or specialized research papers to build expertise.
Tools & Resources
VIT Elective Catalog, Coursera Specializations, ArXiv.org, Towards Data Science blog
Career Connection
Specialized knowledge sets you apart, making you a strong candidate for niche roles like NLP Engineer or Computer Vision Scientist in companies focusing on these AI sub-domains.
Seek Industry Internships and Live Projects- (Semester 3)
Actively apply for internships during summer breaks or semester breaks. Focus on securing opportunities at Indian startups or MNC R&D centers in India to gain direct exposure to industry-grade AI development processes and team environments.
Tools & Resources
LinkedIn Jobs, Internshala, VIT Career Development Centre, Company career pages
Career Connection
Internships are often a direct pipeline to full-time employment, providing invaluable industry experience, professional connections, and a strong resume for placements.
Collaborate on Research and Publish Papers- (Semester 3)
Engage with faculty on research projects, aiming to publish findings in reputable conferences or journals. This hones your research skills, critical thinking, and adds significant weight to your academic profile, especially for research-oriented roles or PhD aspirations.
Tools & Resources
VIT Research Labs, Scopus, IEEE Xplore, Google Scholar
Career Connection
Publications demonstrate research acumen, problem-solving skills, and deep domain knowledge, which are highly valued by R&D divisions and for academic/research career paths.
Advanced Stage
Develop a Comprehensive Capstone Project- (Semester 3-4)
Leverage your accumulated knowledge to undertake a challenging Capstone Project. Aim for a novel solution or a significant contribution to an existing problem, using advanced AI techniques. Focus on deployability and impact.
Tools & Resources
Project management tools (Jira, Trello), Cloud platforms (AWS, Azure, GCP), Docker, Git
Career Connection
A strong capstone project acts as a compelling demonstration of your full skillset to potential employers, showcasing your ability to deliver end-to-end AI solutions in a professional setup.
Network Extensively and Attend Industry Conferences- (Semester 4)
Connect with AI professionals, alumni, and recruiters through LinkedIn, industry events, and college alumni meets. Attending conferences like ''''Cypher'''' or ''''Data Science Congress'''' in India provides insights and direct networking opportunities.
Tools & Resources
LinkedIn, Professional networking events, Industry conferences (Cypher, DSC), Alumni portals
Career Connection
Networking is vital for discovering hidden job markets, gaining referrals, and understanding industry expectations, significantly improving your placement chances in competitive Indian market.
Prepare Rigorously for Placements and Interviews- (Semester 4)
Practice technical coding questions, brush up on theoretical AI concepts, and prepare behavioral answers. Participate in mock interviews conducted by the university or peers. Understand company-specific hiring processes and common interview patterns.
Tools & Resources
GeeksforGeeks, Interviews.ai, Mock Interview Platforms, Company-specific interview guides
Career Connection
Thorough preparation for technical and HR rounds is paramount for converting placement offers. This practice ensures you can confidently articulate your skills and knowledge to recruiters.
Program Structure and Curriculum
Eligibility:
- Bachelor of Engineering / Technology in any branch or Master of Computer Applications (MCA) or M.Sc. in Computer Science / IT / Software Engineering / Electronics / Applied Mathematics / Physics / Statistics / Computational Science or any other equivalent degree from a recognized University / Institute with a minimum overall aggregate of 60% or First Class.
Duration: 2 years (4 semesters)
Credits: 70 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VAM5001 | Mathematical Foundations for AI | Core | 3 | Linear Algebra for AI, Probability and Statistics, Calculus and Optimization, Discrete Mathematics, Random Processes |
| VAI5001 | AI Principles and Techniques | Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms, Knowledge Representation, First-Order Logic |
| VAI5002 | Machine Learning Algorithms | Core | 3 | Supervised Learning, Unsupervised Learning, Ensemble Methods, Model Evaluation Metrics, Reinforcement Learning Introduction |
| VAI5003 | Applied Data Science | Core | 3 | Data Preprocessing, Exploratory Data Analysis, Feature Engineering, Predictive Analytics, Big Data Fundamentals |
| VAI5004 | Data Structures and Algorithms for AI | Core | 3 | Algorithm Analysis, Advanced Data Structures, Graph Algorithms, Dynamic Programming, Computational Complexity |
| VAI5005 | Machine Learning Lab | Lab | 2 | Python for ML, Data Manipulation with Pandas, Scikit-learn Implementation, Model Training and Evaluation, TensorFlow/Keras Basics |
| VAI5006 | Research Methodology for AI | Core | 3 | Research Design, Literature Review Techniques, Data Collection Methods, Statistical Analysis for Research, Technical Report Writing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VAI5007 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer Models, Generative Adversarial Networks (GANs) |
| VAI5008 | Natural Language Processing | Core | 3 | Text Preprocessing, Word Embeddings (Word2Vec, GloVe), Language Models, Sequence Labeling, Machine Translation |
| VAI5009 | Computer Vision | Core | 3 | Image Processing Fundamentals, Feature Detection and Extraction, Object Recognition, Image Segmentation, Deep Learning for Vision Tasks |
| VAI5010 | Ethics and AI | Core | 2 | Ethical Principles in AI, Bias and Fairness in AI, AI Governance and Regulations, Data Privacy and Security, Societal Impact of AI |
| VAI5011 | Deep Learning Lab | Lab | 2 | PyTorch/TensorFlow Implementation, CNNs for Image Classification, RNNs for Sequence Data, Model Training and Hyperparameter Tuning, Deployment of Deep Learning Models |
| PE1 | Programme Elective 1 | Elective | 3 | Topics vary based on student selection from the Programme Elective pool. |
| PE2 | Programme Elective 2 | Elective | 3 | Topics vary based on student selection from the Programme Elective pool. |
| VAI5012 | Natural Language Generation | Elective (Pool) | 3 | Language Models for Generation, Text Summarization, Dialogue Systems, Controllable Text Generation, Evaluation of Generated Text |
| VAI5013 | Explainable AI | Elective (Pool) | 3 | Interpretability vs. Explainability, Local and Global Explanations, LIME, SHAP Techniques, Causal Inference in XAI, Responsible AI |
| VAI5014 | AI for Healthcare | Elective (Pool) | 3 | Medical Image Analysis, Clinical Decision Support Systems, Drug Discovery with AI, Personalized Medicine, Wearable Health Devices |
| VAI5015 | Financial AI | Elective (Pool) | 3 | Algorithmic Trading Strategies, Fraud Detection Systems, Risk Management with AI, Predictive Analytics in Finance, Robo-advisors and Fintech |
| VAI5016 | Robotics and AI | Elective (Pool) | 3 | Robot Kinematics and Dynamics, Path Planning Algorithms, Robot Vision and Perception, Control Systems for Robotics, Human-Robot Interaction |
| VAI5017 | Cognitive Computing | Elective (Pool) | 3 | Human Cognition Models, Knowledge Representation, Automated Reasoning, Cognitive Architectures, Affective Computing |
| VAI5018 | Quantum Machine Learning | Elective (Pool) | 3 | Quantum Computing Fundamentals, Quantum Gates and Circuits, Quantum Algorithms for ML, Quantum Neural Networks, Optimization with Quantum Computing |
| VAI5019 | Advanced Reinforcement Learning | Elective (Pool) | 3 | Actor-Critic Methods, Policy Gradient Algorithms, Model-Based Reinforcement Learning, Multi-Agent RL, Inverse Reinforcement Learning |
| VAI5020 | Speech Processing and AI | Elective (Pool) | 3 | Speech Signal Analysis, Automatic Speech Recognition, Text-to-Speech Synthesis, Speaker Identification, Deep Learning for Audio |
| VAI5021 | Data Stream Mining | Elective (Pool) | 3 | Stream Data Models, Online Learning Algorithms, Concept Drift Detection, Anomaly Detection in Streams, Real-time Analytics |
| VAI5022 | Knowledge Representation and Reasoning | Elective (Pool) | 3 | Ontologies and Semantic Web, Description Logics, Rule-Based Systems, Bayesian Networks, Non-Monotonic Reasoning |
| VAI5023 | Conversational AI | Elective (Pool) | 3 | Chatbot Development, Dialogue Management Systems, Natural Language Understanding, Response Generation, Voice Assistants |
| VAI5024 | AI for Cybersecurity | Elective (Pool) | 3 | Threat Detection with ML, Intrusion Detection Systems, Malware Analysis, Anomaly Detection in Networks, AI for Digital Forensics |
| VAI5025 | Federated Learning | Elective (Pool) | 3 | Decentralized Machine Learning, Privacy-Preserving AI, Secure Multi-Party Computation, Model Aggregation Techniques, Federated Optimization |
| VAI5026 | AI for Cloud Computing | Elective (Pool) | 3 | Cloud Architectures for AI, Distributed AI Systems, Edge AI Deployments, Serverless Machine Learning, Cloud ML Platforms |
| VAI5027 | Advanced Computer Vision | Elective (Pool) | 3 | 3D Computer Vision, Object Tracking, Action Recognition, Generative Vision Models, Image Synthesis |
| VAI5028 | Social Media Analytics and AI | Elective (Pool) | 3 | Sentiment Analysis, Social Network Analysis, Influence Modeling, Trend Prediction, Brand Reputation Management |
| VAI5029 | Explainable AI for Computer Vision | Elective (Pool) | 3 | Saliency Maps, Class Activation Maps, Adversarial Attacks on Vision Models, Model Robustness, Interpretability of CNNs |
| VAI5030 | Intelligent Agents | Elective (Pool) | 3 | Agent Architectures, Rational Agents, Multi-Agent Systems, Game Theory in AI, Belief-Desire-Intention (BDI) Model |
| VAI5031 | Advanced Deep Learning Architectures | Elective (Pool) | 3 | Transformer Networks, Graph Neural Networks (GNNs), Attention Mechanisms, Meta-Learning, Self-Supervised Learning |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VAI6001 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Value and Policy Iteration, Q-Learning and SARSA, Deep Reinforcement Learning, Policy Gradient Methods |
| VAI6002 | AI Project Management | Core | 2 | Agile and Scrum for AI Projects, Project Planning and Execution, Risk Management in AI, Team Collaboration Tools, Deployment and Monitoring |
| VAI6003 | AI Research Seminar | Core | 1 | Research Paper Analysis, Scientific Presentation Skills, Literature Review, Academic Communication, Critiquing Research |
| PE3 | Programme Elective 3 | Elective | 3 | Topics vary based on student selection from the Programme Elective pool. |
| PE4 | Programme Elective 4 | Elective | 3 | Topics vary based on student selection from the Programme Elective pool. |
| UE1 | University Elective 1 | Elective | 3 | Topics vary based on student selection from the broader University Elective pool. |
| VAI6997 | Capstone Project - I | Project | 3 | Problem Identification, Extensive Literature Survey, System Design and Architecture, Methodology Formulation, Initial Implementation and Prototype |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VAI6998 | Capstone Project - II | Project | 12 | Advanced Implementation, Extensive Testing and Validation, Performance Evaluation, Technical Report Writing, Project Presentation and Defense |
