M-TECH in Machine Intelligence And Decision Science at Manipal Institute of Technology
Udupi, Karnataka
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About the Specialization
What is Machine Intelligence and Decision Science at Manipal Institute of Technology Udupi?
This Machine Intelligence and Decision Science program at Manipal Institute of Technology focuses on equipping students with advanced knowledge and skills in artificial intelligence, machine learning, and data-driven decision-making. The curriculum is designed to meet the growing demands of the Indian industry for professionals capable of building intelligent systems and extracting actionable insights from complex data, driving innovation across various sectors.
Who Should Apply?
This program is ideal for engineering graduates (B.E./B.Tech) and science postgraduates (M.Sc.) with a strong aptitude for mathematics and programming, seeking entry into high-tech fields. It also benefits working professionals aiming to upskill in AI/ML, and career changers transitioning into data science roles, provided they meet the foundational academic prerequisites and possess a keen interest in intelligent systems.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding India-specific career paths as AI Engineers, Data Scientists, Machine Learning Architects, and Decision Analysts. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning significantly more. The program prepares students for growth trajectories in major Indian IT firms, startups, and research organizations, fostering expertise aligned with global industry certifications.
Student Success Practices
Foundation Stage
Build a Solid Mathematical & Algorithmic Base- (Semester 1-2)
Dedicate significant time to mastering linear algebra, probability, calculus, and advanced data structures. These are the bedrock of machine intelligence. Regularly solve complex problems to enhance algorithmic thinking and computational efficiency.
Tools & Resources
NPTEL courses on Mathematics for ML, GeeksforGeeks, LeetCode for algorithm practice, MIT OpenCourseWare
Career Connection
Strong fundamentals are crucial for understanding advanced ML concepts and for excelling in technical interviews for AI/ML roles at Indian tech companies.
Master Programming with Relevant Libraries- (Semester 1-2)
Become proficient in Python, specifically its data science ecosystem (NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn). Actively work on mini-projects to apply theoretical knowledge to practical coding challenges.
Tools & Resources
Kaggle notebooks for practice, Coursera Python courses, Official documentation of libraries, GitHub for project showcase
Career Connection
Hands-on coding skills are essential for all data science and machine learning roles. A strong portfolio of projects demonstrates practical capability to recruiters.
Engage in Peer Learning & Discussion Groups- (Semester 1-2)
Form study groups with classmates to discuss complex topics, solve problems collaboratively, and prepare for exams. Teaching others reinforces your own understanding and exposes you to different perspectives.
Tools & Resources
Discord/WhatsApp groups, Collaborative whiteboards, Internal college forums
Career Connection
Develops communication and teamwork skills, critical for collaborative project work in industry. Also, strengthens your network for future career opportunities.
Intermediate Stage
Undertake Industry-Relevant Projects & Internships- (Semester 2-3 (Summer break after Sem 2, Sem 3 focus on project))
Actively seek internships or collaborate on research projects with faculty that involve real-world data and industry challenges. Focus on applying deep learning, NLP, or computer vision to practical problems. This is crucial for gaining practical experience.
Tools & Resources
College placement cell, LinkedIn, Internshala, Departmental research labs, Faculty mentorship
Career Connection
Internships convert into pre-placement offers or provide invaluable industry exposure, making you highly marketable for entry to mid-level roles in Indian tech firms.
Participate in Hackathons & Data Science Competitions- (Semester 2-3)
Regularly participate in online data science competitions and hackathons. This helps in building problem-solving skills under pressure, exploring diverse datasets, and showcasing your abilities beyond academic projects.
Tools & Resources
Kaggle, Analytics Vidhya, HackerRank, College-organized hackathons
Career Connection
Winning or performing well in competitions adds significant weight to your resume, demonstrating practical expertise and a competitive edge to potential employers.
Specialize in a Niche and Build Expertise- (Semester 2-3)
Beyond core subjects, identify an area of interest (e.g., Explainable AI, Reinforcement Learning, specific NLP applications) and delve deeper through elective courses, online certifications, and self-study. Build a portfolio project around this niche.
Tools & Resources
Coursera/edX specialization courses, arXiv for research papers, Open-source projects, Industry-specific blogs
Career Connection
Developing niche expertise makes you a specialist, which is highly valued by companies looking for specific skill sets, potentially leading to better roles and compensation.
Advanced Stage
Focus on Publication & Research Dissemination- (Semester 3-4)
Leverage your Project Work - Phase I & II to produce high-quality research. Aim to publish in reputable conferences or journals, even if it''''s a poster presentation. Actively engage in academic discussions.
Tools & Resources
IEEE Xplore, ACM Digital Library, Scopus, Guide faculty for mentorship on research writing
Career Connection
Publications significantly boost your academic profile for PhD aspirations or distinguish you in R&D roles in large corporations and research institutions in India.
Network Actively & Seek Mentorship- (Semester 3-4)
Attend industry conferences, workshops, and webinars. Connect with professionals, alumni, and faculty. Seek mentors who can guide your career path and provide insights into industry trends and job opportunities.
Tools & Resources
LinkedIn, Professional AI/ML communities (e.g., Data Science Foundation India), College alumni network events
Career Connection
Networking opens doors to exclusive job opportunities, provides crucial career advice, and helps build a strong professional reputation within the Indian AI/ML ecosystem.
Prepare Rigorously for Placements & Interviews- (Semester 3-4)
Begin placement preparation well in advance. Practice coding challenges, behavioral questions, and specialized ML concepts. Prepare a compelling resume and portfolio showcasing your projects and skills.
Tools & Resources
Mock interviews (peer and faculty), InterviewBit, Glassdoor for company-specific interview experiences, Personalized career counseling
Career Connection
Thorough preparation directly translates into higher chances of securing desirable placements in leading Indian and multinational companies during campus recruitment drives.
Program Structure and Curriculum
Eligibility:
- Pass in Bachelor’s degree in Engineering (B.E./B.Tech) or Master’s degree in Science (M.Sc.) in appropriate discipline with minimum 50% aggregate marks or equivalent. Candidates are required to appear for a common entrance test (MET) followed by an interview.
Duration: 2 years (4 semesters)
Credits: 80 Credits
Assessment: Internal: 50% (Continuous Evaluation for Theory/Lab, Internal Evaluation for Project), External: 50% (End Semester Examination for Theory/Lab, Dissertation Viva Voce for Project)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MIM 501 | Mathematical Foundations for Machine Intelligence | Core | 4 | Linear Algebra for ML, Probability Theory & Statistics, Calculus and Optimization, Vector Spaces and Transformations, Random Variables and Distributions |
| MIM 502 | Advanced Data Structures and Algorithms | Core | 4 | Advanced Tree Structures, Graph Algorithms, Dynamic Programming, Greedy Algorithms, Amortized Analysis, NP-Completeness |
| MIM 503 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Ensemble Methods, Model Evaluation and Selection, Bias-Variance Tradeoff, Support Vector Machines |
| MIM 504 | Programming for Machine Intelligence | Core | 4 | Python Fundamentals, Data Manipulation with Pandas, Numerical Computing with NumPy, Data Visualization with Matplotlib, Object-Oriented Programming, Version Control (Git) |
| MIM 505 | Research Methodology and IPR | Core | 2 | Problem Formulation, Literature Review Techniques, Research Design & Methods, Data Collection & Analysis, Technical Report Writing, Intellectual Property Rights |
| MIM 551 | Machine Learning Lab | Lab | 2 | Python for ML Implementations, Data Preprocessing & Cleaning, Classification Algorithms (Scikit-learn), Regression Models, Clustering Techniques, Model Training & Evaluation |
| MIM 552 | Advanced Data Structures and Algorithms Lab | Lab | 2 | Implementation of Trees & Heaps, Graph Traversal Algorithms, Dynamic Programming Solutions, Algorithm Efficiency Analysis, Problem Solving with Data Structures, Code Optimization Techniques |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MIM 506 | Deep Learning | Core | 4 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformers and Attention, Deep Learning Frameworks (TensorFlow/PyTorch) |
| MIM 507 | Statistical Methods for Decision Science | Core | 4 | Hypothesis Testing, Regression Analysis (Linear, Logistic), Time Series Analysis, Multivariate Analysis, Non-parametric Methods, ANOVA and Correlation |
| MIM 508 | Natural Language Processing | Core | 4 | Text Preprocessing, Language Models, Word Embeddings (Word2Vec, GloVe), Sequence Labeling (POS Tagging, NER), Machine Translation, Sentiment Analysis |
| MIM 553 | Deep Learning Lab | Lab | 2 | CNN Implementation for Image Classification, RNNs for Sequence Prediction, Transfer Learning Techniques, Generative Model Training, Hyperparameter Tuning, Deployment of Deep Learning Models |
| MIM 554 | Natural Language Processing Lab | Lab | 2 | NLTK and SpaCy for Text Analysis, Building Custom Language Models, Named Entity Recognition Systems, Text Summarization, Chatbot Development Basics, Topic Modeling |
| MIM 511 | Computer Vision | Elective (Group 1 Option for Slot 1/2) | 4 | Image Filtering & Edge Detection, Feature Extraction (SIFT, HOG), Object Recognition & Detection, Image Segmentation, Deep Learning for Vision, Camera Models & Calibration |
| MIM 512 | Reinforcement Learning | Elective (Group 1 Option for Slot 1/2) | 4 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Q-Learning and SARSA, Policy Gradient Methods, Deep Reinforcement Learning |
| MIM 513 | Big Data Analytics | Elective (Group 1 Option for Slot 1/2) | 4 | Hadoop Ecosystem, Apache Spark, NoSQL Databases, Stream Processing, Data Warehousing, Distributed File Systems |
| MIM 514 | Cognitive Science and AI | Elective (Group 1 Option for Slot 1/2) | 4 | Cognitive Architectures, Perception and Attention, Memory and Learning, Problem Solving & Reasoning, Language and Thought, Human Information Processing |
| MIM 515 | Intelligent Agents and Robotics | Elective (Group 1 Option for Slot 1/2) | 4 | Agent Architectures, Sensorimotor Systems, Robot Kinematics & Dynamics, Path Planning & Navigation, Robot Learning, Multi-Robot Systems |
| MIM 516 | Explainable AI | Elective (Group 1 Option for Slot 1/2) | 4 | Interpretability vs Explainability, Local Explanations (LIME, SHAP), Global Explanations, Feature Importance Methods, Counterfactual Explanations, Ethical Implications of XAI |
| MIM 517 | Blockchain Technologies | Elective (Group 1 Option for Slot 1/2) | 4 | Cryptographic Primitives, Distributed Ledger Technology, Consensus Mechanisms, Smart Contracts, Decentralized Applications (DApps), Blockchain Platforms (Ethereum, Hyperledger) |
| MIM 518 | Game Theory for Decision Making | Elective (Group 1 Option for Slot 1/2) | 4 | Normal Form Games, Extensive Form Games, Nash Equilibrium, Cooperative Games, Mechanism Design, Repeated Games |
| MIM 519 | Quantum Machine Learning | Elective (Group 1 Option for Slot 1/2) | 4 | Quantum Computing Basics, Qubits and Quantum Gates, Quantum Algorithms (Grover''''s, Shor''''s), Quantum Neural Networks, Quantum Optimization, Quantum Machine Learning Applications |
| MIM 520 | Ethical AI and Governance | Elective (Group 1 Option for Slot 1/2) | 4 | AI Ethics Principles, Bias and Fairness in AI, AI Privacy Concerns, Accountability and Transparency, Regulatory Frameworks for AI, Societal Impact of AI |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MIM 601 | Project Work - Phase I | Project | 6 | Problem Identification, Extensive Literature Survey, Detailed Project Proposal, Methodology Design & Planning, Initial Implementation & Experimentation, Interim Report Submission |
| MIM 611 | Advanced Computer Vision | Elective (Group 2 Option for Slot 3/4) | 4 | 3D Computer Vision, Generative Models for Vision, Video Analysis & Tracking, Human Pose Estimation, Medical Image Analysis, Autonomous Driving Perception |
| MIM 612 | Bayesian Machine Learning | Elective (Group 2 Option for Slot 3/4) | 4 | Bayesian Inference, Prior and Posterior Distributions, Markov Chain Monte Carlo (MCMC), Gaussian Processes, Bayesian Networks, Variational Inference |
| MIM 613 | Graph Neural Networks | Elective (Group 2 Option for Slot 3/4) | 4 | Graph Representation Learning, Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Recurrent Graph Neural Networks, Applications of GNNs, Graph Embedding Techniques |
| MIM 614 | Time Series Analysis and Forecasting | Elective (Group 2 Option for Slot 3/4) | 4 | ARIMA Models, Exponential Smoothing, State Space Models, Deep Learning for Time Series, Forecasting Techniques, Seasonality and Trend Analysis |
| MIM 615 | Multi-agent Systems | Elective (Group 2 Option for Slot 3/4) | 4 | Agent Communication Protocols, Coordination and Cooperation, Negotiation Strategies, Distributed Problem Solving, Swarm Intelligence, Game Theory in MAS |
| MIM 616 | Generative Adversarial Networks | Elective (Group 2 Option for Slot 3/4) | 4 | GAN Architectures, DCGAN and Conditional GANs, CycleGAN and StyleGAN, Adversarial Training Techniques, Image Generation and Style Transfer, GAN Applications |
| MIM 617 | Causal Inference and Decision Making | Elective (Group 2 Option for Slot 3/4) | 4 | Causal Graphs (DAGs), Counterfactuals, Causal Discovery Algorithms, Instrumental Variables, Average Treatment Effect, Decision Making under Uncertainty |
| MIM 618 | Federated Learning | Elective (Group 2 Option for Slot 3/4) | 4 | Decentralized Machine Learning, Privacy-Preserving AI, Homomorphic Encryption, Secure Aggregation, Communication Efficiency, Differential Privacy in FL |
| MIM 619 | Human-Computer Interaction | Elective (Group 2 Option for Slot 3/4) | 4 | User-Centered Design Principles, Usability Evaluation Methods, Interaction Design Paradigms, User Research & Prototyping, Cognitive Aspects of HCI, Accessibility in Design |
| MIM 620 | Knowledge Representation and Reasoning | Elective (Group 2 Option for Slot 3/4) | 4 | Ontologies and Semantic Web, Logic Programming (Prolog), Semantic Networks, Rule-based Systems, Expert Systems, Uncertainty in Knowledge Systems |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MIM 602 | Project Work - Phase II | Project | 18 | Advanced System Implementation, Extensive Experimental Evaluation, In-depth Data Analysis & Interpretation, Comprehensive Thesis Writing, Oral Presentation and Viva Voce, Publication Opportunities |
| MIM 611 | Advanced Computer Vision | Elective (Group 2 Option for Slot 5) | 4 | 3D Computer Vision, Generative Models for Vision, Video Analysis & Tracking, Human Pose Estimation, Medical Image Analysis, Autonomous Driving Perception |
| MIM 612 | Bayesian Machine Learning | Elective (Group 2 Option for Slot 5) | 4 | Bayesian Inference, Prior and Posterior Distributions, Markov Chain Monte Carlo (MCMC), Gaussian Processes, Bayesian Networks, Variational Inference |
| MIM 613 | Graph Neural Networks | Elective (Group 2 Option for Slot 5) | 4 | Graph Representation Learning, Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Recurrent Graph Neural Networks, Applications of GNNs, Graph Embedding Techniques |
| MIM 614 | Time Series Analysis and Forecasting | Elective (Group 2 Option for Slot 5) | 4 | ARIMA Models, Exponential Smoothing, State Space Models, Deep Learning for Time Series, Forecasting Techniques, Seasonality and Trend Analysis |
| MIM 615 | Multi-agent Systems | Elective (Group 2 Option for Slot 5) | 4 | Agent Communication Protocols, Coordination and Cooperation, Negotiation Strategies, Distributed Problem Solving, Swarm Intelligence, Game Theory in MAS |
| MIM 616 | Generative Adversarial Networks | Elective (Group 2 Option for Slot 5) | 4 | GAN Architectures, DCGAN and Conditional GANs, CycleGAN and StyleGAN, Adversarial Training Techniques, Image Generation and Style Transfer, GAN Applications |
| MIM 617 | Causal Inference and Decision Making | Elective (Group 2 Option for Slot 5) | 4 | Causal Graphs (DAGs), Counterfactuals, Causal Discovery Algorithms, Instrumental Variables, Average Treatment Effect, Decision Making under Uncertainty |
| MIM 618 | Federated Learning | Elective (Group 2 Option for Slot 5) | 4 | Decentralized Machine Learning, Privacy-Preserving AI, Homomorphic Encryption, Secure Aggregation, Communication Efficiency, Differential Privacy in FL |
| MIM 619 | Human-Computer Interaction | Elective (Group 2 Option for Slot 5) | 4 | User-Centered Design Principles, Usability Evaluation Methods, Interaction Design Paradigms, User Research & Prototyping, Cognitive Aspects of HCI, Accessibility in Design |
| MIM 620 | Knowledge Representation and Reasoning | Elective (Group 2 Option for Slot 5) | 4 | Ontologies and Semantic Web, Logic Programming (Prolog), Semantic Networks, Rule-based Systems, Expert Systems, Uncertainty in Knowledge Systems |