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M-TECH in Artificial Intelligence Data Science at Datta Meghe Institute of Medical Sciences (Deemed to be University)
Wardha, Maharashtra
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About the Specialization
What is Artificial Intelligence & Data Science at Datta Meghe Institute of Medical Sciences (Deemed to be University) Wardha?
This M.Tech Artificial Intelligence & Data Science program at Datta Meghe Institute of Higher Education and Research focuses on equipping students with advanced knowledge and practical skills in AI, machine learning, deep learning, and big data analytics. It addresses the escalating demand for skilled professionals in India''''s rapidly expanding AI and data science sectors, preparing graduates for impactful roles in innovation and research. The program emphasizes both theoretical foundations and hands-on application, making it highly relevant to industry needs.
Who Should Apply?
This program is ideal for engineering graduates in computer science, IT, or related fields, as well as MCA or M.Sc. (CS/IT/Mathematics/Statistics) holders seeking to specialize in cutting-edge AI and data science domains. It caters to fresh graduates aiming for high-growth tech careers and working professionals aspiring to upskill for leadership roles in data-driven industries. Candidates with a strong analytical aptitude and a foundational understanding of programming are well-suited.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths in India as AI Engineers, Data Scientists, Machine Learning Engineers, Big Data Analysts, and Research Scientists. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals commanding significantly higher packages. The comprehensive curriculum prepares students for roles in startups, MNCs, and research institutions, aligning with industry certifications in areas like cloud platforms and specialized AI/ML tools.
Student Success Practices
Foundation Stage
Master Core Programming & Math- (Semester 1)
Deeply understand data structures, algorithms, and advanced programming concepts (Python, Java). Simultaneously, solidify mathematical foundations in linear algebra, calculus, and probability, crucial for AI/ML. Utilize online platforms for competitive programming and problem-solving.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Khan Academy, NPTEL courses on DSA and Probability & Statistics
Career Connection
Strong fundamentals are indispensable for cracking technical interviews and building efficient AI/ML models, setting a solid base for advanced topics.
Hands-on Lab Implementation- (Semester 1)
Actively engage in all lab sessions for Machine Learning, AI, and Deep Learning. Translate theoretical concepts into practical code, experiment with different algorithms, and understand their real-world implications. Document all experiments and results thoroughly.
Tools & Resources
Python with libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Jupyter Notebooks, Google Colab, Anaconda
Career Connection
Practical experience with popular ML frameworks is highly valued by employers, showcasing problem-solving and implementation skills for entry-level roles.
Peer Learning & Study Groups- (Semester 1)
Form study groups with peers to discuss complex topics, solve problems collaboratively, and prepare for examinations. Teach concepts to others to reinforce your own understanding. Participate in college-level coding challenges or hackathons to apply knowledge.
Tools & Resources
Discord/WhatsApp groups, Whiteboards, Peer-to-peer coding sessions, Internal college hackathons
Career Connection
Enhances communication, teamwork, and collaborative problem-solving skills, which are crucial for success in professional team environments in Indian tech companies.
Intermediate Stage
Deep Dive into Specialized Areas- (Semester 2)
Choose electives strategically based on career interests (e.g., NLP, Computer Vision, Data Mining). Explore advanced topics like Deep Learning, Big Data Analytics, and Cloud Computing beyond the curriculum through online courses and specialized projects.
Tools & Resources
Coursera, edX, Udemy courses, Kaggle competitions, Official documentation for Hadoop, Spark, AWS/Azure
Career Connection
Specialization enhances employability by creating a niche skill set, leading to roles in specific AI/ML sub-fields, a key differentiator in the Indian job market.
Industry-Relevant Mini-Projects- (Semester 2)
Actively participate in the Mini Project (MAI02P3). Identify real-world problems and apply learned AI/ML and big data techniques to develop practical solutions. Focus on end-to-end project development, including data collection, model building, and deployment.
Tools & Resources
GitHub for version control, Industry-standard datasets (e.g., from UCI Machine Learning Repository, Kaggle), Cloud platforms for deployment (e.g., Heroku, Streamlit)
Career Connection
Building a strong project portfolio is critical for demonstrating practical skills and securing internships or job offers, especially for product-based companies.
Networking and Knowledge Sharing- (Semester 2)
Attend webinars, workshops, and conferences (virtual or local) focused on AI, Data Science, and Cloud technologies. Connect with industry professionals and researchers. Participate in tech forums and contribute to open-source projects where possible.
Tools & Resources
LinkedIn, Meetup groups, Academic conferences (e.g., AAAI, ICML local chapters in India), Tech communities on platforms like Stack Overflow
Career Connection
Builds a professional network, opens doors to opportunities, and keeps students updated with industry trends, fostering career advancement.
Advanced Stage
Strategic Industrial Training & Research Project- (Semester 3-4)
Undertake mandatory Industrial Training/Internship (MAI03PW1) at a reputable company to gain practical industry experience. Dedicate significant effort to Project Work - Part I and II (MAI03P1, MAI04PW1), aiming for an impactful research or application-oriented project.
Tools & Resources
Company-provided resources during internship, Research papers (arXiv, IEEE Xplore), Academic advisors, Specialized software/hardware for project implementation
Career Connection
Industrial experience is often a prerequisite for placements, and a strong final project can be a significant resume highlight, demonstrating readiness for complex roles.
Placement Preparation & Interview Skills- (Semester 3-4)
Begin focused preparation for placements well in advance. Practice aptitude tests, technical interviews (data structures, algorithms, ML concepts), and soft skills (communication, presentation). Prepare a compelling resume and LinkedIn profile highlighting projects and skills.
Tools & Resources
Mock interviews (peer or professional), Online aptitude platforms (e.g., PrepInsta, IndiaBix), Company-specific interview guides on platforms like Glassdoor, University career counseling services
Career Connection
Directly impacts job offer success, leading to successful entry into the AI & Data Science industry across various Indian tech sectors.
Advanced Skill Refinement & Leadership- (Semester 3-4)
Pursue advanced certifications in cloud AI/ML services (AWS ML Specialty, Azure AI Engineer) or specific ML frameworks. Take initiative in mentoring junior students or leading technical clubs. Focus on publishing research if the final project yields novel results, contributing to academic or industrial innovation.
Tools & Resources
Official certification guides and practice exams, Advanced online courses (e.g., deeplearning.ai), University mentorship programs, Research journals and conferences for publication
Career Connection
Differentiates candidates, demonstrates leadership potential, and positions graduates for accelerated career growth or further academic pursuits, making them future industry leaders.
Program Structure and Curriculum
Eligibility:
- B.E. / B.Tech. in Computer Engineering / Information Technology / Computer Science & Engineering / Electronics & Communication Engineering / Electrical Engineering / Electronics Engineering or equivalent with minimum 50% marks (45% for backward class) OR M.Sc. (Computer Science / IT / Mathematics / Statistics) or MCA or equivalent.
Duration: 4 semesters / 2 years
Credits: 90 Credits
Assessment: Internal: 40% (for theory subjects), 50% (for practical subjects), External: 60% (for theory subjects), 50% (for practical subjects)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAI01T1 | Advanced Data Structures | Core | 4 | Data structures concepts and operations, Linear data structures (stacks, queues, linked lists), Non-linear data structures (trees, graphs), Searching and sorting algorithms, Hashing techniques and applications |
| MAI01T2 | Machine Learning | Core | 4 | Introduction to machine learning paradigms, Supervised learning algorithms (regression, classification), Unsupervised learning (clustering, dimensionality reduction), Model evaluation and selection techniques, Ensemble methods and boosting |
| MAI01T3 | Artificial Intelligence | Core | 4 | Intelligent agents and problem solving, Search algorithms (uninformed, informed), Knowledge representation and reasoning, First-order logic and inference mechanisms, Planning and probabilistic reasoning |
| MAI01T4 | Advanced Database Management Systems | Core | 4 | Relational database model and query languages, Query processing and optimization, Transaction management and concurrency control, Distributed database systems, NoSQL databases and big data storage |
| MAI01P1 | Advanced Data Structures Lab | Lab | 2 | Implementation of linear data structures, Implementation of tree and graph algorithms, Practical application of searching and sorting, Hashing techniques implementation, Problem-solving using advanced data structures |
| MAI01P2 | Machine Learning Lab | Lab | 2 | Data preprocessing and feature engineering, Implementation of supervised learning algorithms, Implementation of unsupervised learning algorithms, Model training, evaluation, and hyperparameter tuning, Working with ML libraries (e.g., scikit-learn) |
| MAI01P3 | Artificial Intelligence Lab | Lab | 2 | Implementation of AI search algorithms, Logic programming using Prolog, Developing simple AI agents, Knowledge representation experiments, AI problem-solving with Python |
| MAI01RM | Research Methodology & IPR | Core | 3 | Foundations of research: problem identification, literature review, Research design and data collection methods, Statistical analysis for research, Report writing and ethical considerations, Intellectual Property Rights (IPR) fundamentals |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAI02T1 | Deep Learning | Core | 4 | Introduction to neural networks and deep learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Deep learning architectures (e.g., Transformers, GANs), Optimization techniques and regularization |
| MAI02T2 | Big Data Analytics | Core | 4 | Introduction to big data and its challenges, Hadoop ecosystem: HDFS, MapReduce, Apache Spark for big data processing, NoSQL databases for scalable storage, Big data querying and stream processing |
| MAI02T3 | Cloud Computing | Core | 4 | Cloud computing models (IaaS, PaaS, SaaS), Virtualization and containerization technologies, Cloud storage and networking concepts, Cloud security and management, Introduction to major cloud platforms (AWS, Azure, GCP) |
| MAI02OE | Open Elective - I | Elective | 3 | Options: Natural Language Processing, Computer Vision, Reinforcement Learning |
| MAI02OE1 | Natural Language Processing (Elective Option) | Elective | 3 | Text preprocessing and linguistic phenomena, N-grams and language modeling, Word embeddings (Word2Vec, GloVe), Syntactic parsing and semantic analysis, Machine translation and text generation |
| MAI02OE2 | Computer Vision (Elective Option) | Elective | 3 | Image formation and perception, Image preprocessing and feature extraction, Object detection and recognition, Image segmentation techniques, Deep learning for computer vision applications |
| MAI02OE3 | Reinforcement Learning (Elective Option) | Elective | 3 | Markov Decision Processes (MDPs), Dynamic programming and Monte Carlo methods, Q-learning and SARSA algorithms, Policy gradient methods, Deep Reinforcement Learning applications |
| MAI02E | Elective - I | Elective | 3 | Options: Data Mining, Soft Computing, Internet of Things, Quantum Computing |
| MAI02E1 | Data Mining (Elective Option) | Elective | 3 | Data preprocessing and data warehousing, Association rule mining, Classification techniques (decision trees, SVM), Clustering algorithms (K-Means, hierarchical), Web mining and text mining |
| MAI02E2 | Soft Computing (Elective Option) | Elective | 3 | Introduction to fuzzy logic and fuzzy sets, Artificial neural networks architectures, Genetic algorithms and evolutionary computation, Hybrid soft computing techniques, Applications of soft computing |
| MAI02E3 | Internet of Things (Elective Option) | Elective | 3 | IoT architecture and communication protocols, Sensors, actuators, and embedded systems, IoT data analytics and cloud integration, IoT security and privacy considerations, Applications of IoT in smart environments |
| MAI02E4 | Quantum Computing (Elective Option) | Elective | 3 | Fundamentals of quantum mechanics, Quantum bits (qubits) and quantum gates, Quantum entanglement and superposition, Quantum algorithms (Shor''''s, Grover''''s), Quantum supremacy and hardware challenges |
| MAI02P1 | Deep Learning Lab | Lab | 2 | Implementing CNNs for image classification, Developing RNNs for sequence data, Working with deep learning frameworks (TensorFlow, PyTorch), Building generative adversarial networks (GANs), Experimenting with transfer learning |
| MAI02P2 | Big Data Analytics Lab | Lab | 2 | Setting up Hadoop and performing MapReduce tasks, Processing data with Apache Spark, Using Pig and Hive for data analysis, Working with NoSQL databases (e.g., MongoDB, Cassandra), Implementing stream processing applications |
| MAI02P3 | Mini Project | Project | 2 | Project problem definition and scope, Literature survey and methodology design, Implementation and initial testing, Report writing and presentation, Individual or group project development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAI03OE | Open Elective - II | Elective | 3 | Options: Business Intelligence & Data Warehousing, Information Retrieval, Blockchain Technology |
| MAI03OE1 | Business Intelligence & Data Warehousing (Elective Option) | Elective | 3 | Data warehousing concepts and architecture, ETL processes and data modeling, Online Analytical Processing (OLAP), Business intelligence tools and dashboards, Data reporting and analytics for decision making |
| MAI03OE2 | Information Retrieval (Elective Option) | Elective | 3 | IR models (Boolean, Vector Space, Probabilistic), Query processing and document ranking, Evaluation metrics for IR systems, Web search engines and crawling, Recommender systems foundations |
| MAI03OE3 | Blockchain Technology (Elective Option) | Elective | 3 | Cryptographic primitives and hash functions, Distributed ledger technology fundamentals, Consensus algorithms (PoW, PoS), Smart contracts and DApps, Blockchain platforms (Ethereum, Hyperledger) |
| MAI03E | Elective - II | Elective | 3 | Options: High Performance Computing, Digital Image Processing, Cognitive Computing |
| MAI03E1 | High Performance Computing (Elective Option) | Elective | 3 | Parallel computing architectures, Distributed memory systems and message passing, Cluster and grid computing, GPU computing with CUDA/OpenCL, Performance optimization techniques |
| MAI03E2 | Digital Image Processing (Elective Option) | Elective | 3 | Image enhancement techniques, Image restoration and filtering, Image segmentation methods, Feature extraction and representation, Image compression standards |
| MAI03E3 | Cognitive Computing (Elective Option) | Elective | 3 | Cognitive architectures and AI systems, Natural language understanding and generation, Machine perception and sensory processing, Learning, memory, and reasoning in AI, Cognitive systems applications and challenges |
| MAI03E | Elective - III | Elective | 3 | Options: Cyber Security & Forensics, Recommender Systems, Data Visualization |
| MAI03E4 | Cyber Security & Forensics (Elective Option) | Elective | 3 | Network security protocols and attacks, Cryptography and secure communication, Cybercrime investigation techniques, Digital forensics methodologies, Security policies and legal aspects |
| MAI03E5 | Recommender Systems (Elective Option) | Elective | 3 | Collaborative filtering techniques, Content-based recommendation systems, Hybrid recommendation approaches, Evaluation metrics and cold-start problem, Ethical considerations in recommender systems |
| MAI03E6 | Data Visualization (Elective Option) | Elective | 3 | Principles of visual perception and design, Types of data visualizations and charts, Interactive visualization techniques, Data storytelling and dashboard design, Tools like Tableau, Power BI, D3.js |
| MAI03P1 | Project Work - Part I | Project | 8 | Problem identification and definition, Extensive literature review, Methodology and system design, Initial implementation and experimental setup, Progress report and presentation |
| MAI03PW1 | Industrial Training / Internship | Project | 4 | On-site industry exposure and practical application, Participation in real-world projects, Learning industry best practices, Problem-solving in an industrial setting, Submission of training report and presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAI04PW1 | Project Work - Part II | Project | 18 | Advanced implementation and development, Thorough testing and validation, Comprehensive results analysis and interpretation, Thesis writing and documentation, Final presentation and viva-voce examination |
