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B-TECH in Artificial Intelligence And Machine Learning at Datta Meghe Institute of Medical Sciences (Deemed to be University)
Wardha, Maharashtra
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About the Specialization
What is Artificial Intelligence and Machine Learning at Datta Meghe Institute of Medical Sciences (Deemed to be University) Wardha?
This B.Tech Artificial Intelligence and Machine Learning program at Datta Meghe Institute of Higher Education and Research focuses on equipping students with advanced theoretical knowledge and practical skills in AI, ML, and Deep Learning. It integrates core computer science principles with specialized topics relevant to the rapidly evolving Indian tech industry. The program aims to foster innovation and problem-solving capabilities, preparing graduates for cutting-edge roles.
Who Should Apply?
This program is ideal for high school graduates with a strong foundation in Mathematics and Science who are passionate about data, algorithms, and intelligent systems. It caters to aspiring AI engineers, data scientists, and machine learning researchers. Working professionals seeking to upskill in AI/ML or career changers transitioning into the high-demand field of artificial intelligence in India will also find this program highly beneficial.
Why Choose This Course?
Graduates of this program can expect promising career paths in India as AI engineers, machine learning specialists, data scientists, and robotics engineers. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning upwards of INR 15-30+ LPA in leading Indian tech firms and startups. The curriculum aligns with requirements for various industry certifications, enhancing growth trajectories in the vibrant Indian AI ecosystem.
Student Success Practices
Foundation Stage
Master Core Programming & Math- (Semester 1-2)
Dedicate significant time in Semesters 1 and 2 to build a strong foundation in C/Python programming, data structures, and engineering mathematics. These are the bedrock for advanced AI/ML concepts. Consistent practice and understanding of algorithms are crucial.
Tools & Resources
HackerRank, GeeksforGeeks, NPTEL online courses for Mathematics, Coursera Introduction to Programming in Python
Career Connection
A strong foundation in these areas directly impacts your ability to grasp complex AI/ML algorithms and excel in technical interviews for entry-level roles.
Engage in Early Project-Based Learning- (Semester 1-2)
Start engaging in small, self-initiated projects using basic Python and ML libraries. Even simple projects like sentiment analysis or house price prediction using small datasets can solidify understanding and build a portfolio early on.
Tools & Resources
Kaggle (for datasets), Jupyter Notebook, Scikit-learn documentation, GitHub for project showcase
Career Connection
Early practical experience and a GitHub portfolio demonstrate initiative and practical skills to recruiters, setting you apart from peers.
Participate in Tech Clubs & Peer Learning- (Semester 1-2)
Join the college''''s coding or AI/ML clubs. Actively participate in weekly discussions, workshops, and coding challenges. Forming study groups with peers helps clarify doubts and fosters collaborative problem-solving.
Tools & Resources
College technical clubs, Discord/Telegram groups for peer learning, Local hackathons
Career Connection
Develops teamwork, communication, and networking skills, which are highly valued in corporate environments. Also, exposes you to diverse problem-solving approaches.
Intermediate Stage
Deep Dive into ML/DL Frameworks- (Semester 3-5)
Beyond theoretical knowledge, focus on practical implementation using industry-standard frameworks like TensorFlow and PyTorch. Build and train models for specific tasks, understanding their architectures and optimization techniques.
Tools & Resources
TensorFlow/Keras official documentation, PyTorch tutorials, Google Colab for GPU access, Fast.ai courses
Career Connection
Proficiency in these frameworks is a direct requirement for most AI/ML engineering roles, enabling you to build deployable solutions.
Undertake Mini-Projects and Internships- (Semester 4-6)
Leverage your growing skills by undertaking mini-projects with real-world datasets and seeking summer internships. This provides crucial industry exposure, applies academic knowledge to practical scenarios, and builds a professional network.
Tools & Resources
LinkedIn for internship search, Internshala, College placement cell, Open-source contribution platforms
Career Connection
Internships are often the gateway to full-time employment and offer invaluable insights into industry demands and work culture.
Develop Strong Data Handling Skills- (Semester 3-5)
Focus on data preprocessing, cleaning, feature engineering, and database management. Mastering SQL, Python''''s Pandas library, and understanding Big Data concepts like Hadoop/Spark is critical for any data-centric AI/ML role.
Tools & Resources
SQL Practice platforms, Pandas documentation, Databricks Community Edition for Spark, Big Data courses on edX/Coursera
Career Connection
Data scientists and ML engineers spend a significant portion of their time on data preparation. Strong data handling skills make you highly efficient and marketable.
Advanced Stage
Pursue Advanced Specialization & Research- (Semester 6-8)
In later semesters, specialize in a niche area of AI/ML (e.g., NLP, Computer Vision, Reinforcement Learning, Generative AI) through electives and research projects. Actively participate in research activities, paper reading groups, or publish findings.
Tools & Resources
ArXiv for research papers, Google Scholar, Departmental research labs, Advanced MOOCs on specialized topics
Career Connection
Specialized knowledge opens doors to advanced research roles, R&D departments, and roles requiring deep expertise in a specific AI domain.
Focus on Industry Readiness & Placements- (Semester 7-8)
Attend placement preparation workshops, mock interviews, and technical assessment training sessions. Polish your resume, practice aptitude and coding questions, and be prepared to articulate your project experiences effectively. Build a strong portfolio of projects.
Tools & Resources
Placement cell resources, LeetCode/HackerRank for coding practice, Glassdoor for interview experiences, Resume/Cover letter builders
Career Connection
This phase is directly aimed at securing top placements or admissions to higher studies. Strong preparation ensures you convert opportunities into successful career beginnings.
Develop Soft Skills & Ethical AI Understanding- (Semester 6-8)
Participate in communication and presentation skill development programs. Simultaneously, delve into the ethical implications of AI, understanding biases, fairness, and accountability in AI systems. This is increasingly critical in the industry.
Tools & Resources
Toastmasters International (if available), TED Talks for inspiration, Courses on Ethical AI and AI Governance, Case studies on AI ethics
Career Connection
Beyond technical skills, companies seek well-rounded individuals. Strong soft skills are vital for collaboration and leadership, while ethical awareness prepares you for responsible AI development, a growing concern in Indian tech.
Program Structure and Curriculum
Eligibility:
- Candidates must have passed 10+2 examination with Physics, Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Technical Vocational subject/Computer Science/Information Technology/Informatics Practices/Agriculture/Engineering Graphics/Business Studies. Obtained at least 45% marks (40% in case of candidates belonging to reserved category) in the above subjects taken together.
Duration: 4 years (8 semesters)
Credits: 169 Credits
Assessment: Internal: 20% (Theory), 40% (Practical), External: 80% (Theory), 60% (Practical)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTBSC101 | Engineering Mathematics-I | Core | 4 | Matrices and System of Linear Equations, Differential Calculus, Multivariable Calculus, Ordinary Differential Equations, Laplace Transform |
| BTBSC102 | Engineering Physics | Core | 4 | Wave Optics, Quantum Mechanics, Solid State Physics, Lasers and Holography, Fiber Optics |
| BTHSC103 | English | Core | 2 | Functional Grammar, Reading Comprehension, Writing Skills, Oral Communication, Vocabulary Building |
| BTPCC104 | Programming for Problem Solving | Core | 3 | Introduction to Programming, Control Flow Statements, Functions and Recursion, Arrays and Pointers, Structures and Union |
| BTPCC105 | AI and Machine Learning Concepts | Core | 3 | Introduction to AI, Machine Learning Basics, Data Preprocessing, Supervised Learning Concepts, Unsupervised Learning Concepts |
| BTHSCL106 | English Language Lab | Lab | 1 | Phonetics and Pronunciation, Presentation Skills, Group Discussion Techniques, Role-playing Scenarios, Interview Skills |
| BTBSCL107 | Engineering Physics Lab | Lab | 1 | Experiments on Optics, Semiconductor Devices, Lasers Characteristics, Magnetic Field Measurement, Resonance Phenomena |
| BTPCCL108 | Programming for Problem Solving Lab | Lab | 1 | Basic C Programs, Conditional Statements and Loops, Functions and Arrays Implementation, Pointers and String Manipulation, Structure and File Operations |
| BTPCCL109 | AI and Machine Learning Concepts Lab | Lab | 1 | Data Handling with Python, Basic Classification Algorithms, Data Visualization Techniques, Model Evaluation Metrics, Simple Regression Implementation |
| BTESC110 | Engineering Graphics and Design | Core | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Introduction to CAD |
| BTESCL111 | Engineering Graphics and Design Lab | Lab | 1 | Drafting using CAD Software, 2D and 3D Modeling, Assembly Drawing, Part Modeling, Dimensioning and Tolerancing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTBSC201 | Engineering Mathematics-II | Core | 4 | Integral Calculus, Vector Calculus, Complex Numbers, Probability and Statistics, First Order Differential Equations |
| BTBSC202 | Engineering Chemistry | Core | 4 | Water Technology, Fuels and Combustion, Electrochemistry, Corrosion and its Control, Polymer Chemistry |
| BTHSC203 | Universal Human Values | Core | 2 | Self-exploration and Self-awareness, Understanding Harmony in Human Relationships, Harmony in Society, Harmony in Nature, Professional Ethics |
| BTESC204 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Magnetic Circuits, Transformers, Electrical Machines |
| BTESC205 | Basic Electronics Engineering | Core | 3 | Semiconductor Diodes and Applications, Bipolar Junction Transistors, Field Effect Transistors, Operational Amplifiers, Digital Logic Gates |
| BTBSCL206 | Engineering Chemistry Lab | Lab | 1 | Water Analysis, Volumetric Analysis, Instrumental Analysis, Synthesis of Polymers, Lubricant Properties |
| BTESCL207 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Network Theorems, Study of RLC Circuits, Three Phase Systems, Transformer Characteristics, Measurement of Power |
| BTESCL208 | Basic Electronics Engineering Lab | Lab | 1 | Diode Characteristics, Rectifier Circuits, Transistor Amplifier Design, Op-Amp Applications, Digital Logic Gate Verification |
| BTESCL209 | Workshop/Manufacturing Practices | Lab | 2 | Carpentry and Fitting, Welding Techniques, Sheet Metal Operations, Machine Shop Practices, Foundry Practices |
| BTBSC210 | Environmental Science | Core | 0 | Natural Resources, Ecosystems and Biodiversity, Environmental Pollution, Waste Management, Environmental Legislation and Ethics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTBSC301 | Engineering Mathematics-III | Core | 4 | Fourier Series and Transforms, Z-transform, Partial Differential Equations, Numerical Methods, Linear Algebra for Engineers |
| BTPCC302 | Data Structure | Core | 3 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms |
| BTPCC303 | Object-Oriented Programming | Core | 3 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Exception Handling, File Handling |
| BTPCC304 | Computer Organization and Architecture | Core | 3 | Basic Computer Organization, CPU Organization, Memory Organization, Input/Output Organization, Pipelining and Parallel Processing |
| BTPCC305 | Discrete Mathematics | Core | 3 | Set Theory and Logic, Relations and Functions, Counting and Combinatorics, Graph Theory, Algebraic Structures |
| BTPCCL306 | Data Structure Lab | Lab | 1 | Implementation of Arrays and Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Programs |
| BTPCCL307 | Object-Oriented Programming Lab | Lab | 1 | Classes and Objects Implementation, Inheritance and Polymorphism Exercises, Abstract Classes and Interfaces, Exception Handling Programs, File I/O Operations |
| BTAIC308 | Data Communication and Computer Network | Core | 3 | Network Models (OSI, TCP/IP), Physical and Data Link Layer, Network Layer Protocols, Transport Layer Protocols, Application Layer Services |
| BTAICL309 | Data Communication and Computer Network Lab | Lab | 1 | Network Configuration Commands, Packet Tracing and Analysis, Socket Programming Basics, Network Device Simulation, Basic Network Security |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTHSC401 | Economics for Engineers | Core | 2 | Basic Economic Concepts, Demand and Supply Analysis, Market Structures, National Income and Inflation, Project Evaluation Techniques |
| BTPCC402 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis Techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms and NP-Completeness |
| BTPCC403 | Operating System | Core | 3 | Operating System Structures, Process Management, CPU Scheduling, Memory Management, File Systems and I/O Management |
| BTPCC404 | Database Management System | Core | 3 | Introduction to DBMS, Relational Model and Algebra, Structured Query Language (SQL), Database Design (ER Model, Normalization), Transaction Management and Concurrency Control |
| BTAIC405 | Python for AI & ML | Core | 3 | Python Fundamentals, Data Structures in Python, NumPy for Numerical Computing, Pandas for Data Analysis, Matplotlib for Data Visualization |
| BTPCCL406 | Design and Analysis of Algorithms Lab | Lab | 1 | Sorting and Searching Algorithm Implementations, Graph Traversal Algorithms, Dynamic Programming Solutions, Greedy Algorithm Applications, Computational Complexity Analysis |
| BTPCCL407 | Operating System Lab | Lab | 1 | Shell Scripting, Process Creation and Management, CPU Scheduling Algorithms, Memory Allocation Strategies, Synchronization Problems |
| BTPCCL408 | Database Management System Lab | Lab | 1 | SQL Queries (DDL, DML, DCL), Joins and Subqueries, Database Schema Design, Triggers and Stored Procedures, NoSQL Database Basics |
| BTAICL409 | Python for AI & ML Lab | Lab | 1 | Python Programming Practice, Data Manipulation with Pandas, Data Visualization with Matplotlib/Seaborn, File Operations in Python, Basic Scripting for Automation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTHSC501 | Management I | Core | 3 | Principles of Management, Planning and Decision Making, Organizing and Staffing, Directing and Controlling, Organizational Behavior |
| BTAIC502 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Model Evaluation and Selection, Feature Engineering, Ensemble Methods |
| BTAIC503 | Artificial Intelligence | Core | 3 | Introduction to AI and Intelligent Agents, Problem-Solving through Search, Knowledge Representation and Reasoning, Logical Agents and Planning, Expert Systems |
| BTAIC504 | Deep Learning | Core | 3 | Fundamentals of Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Learning Frameworks (TensorFlow, PyTorch) |
| BTAIE505X | Departmental Elective-I (e.g., Natural Language Processing) | Elective | 3 | Text Preprocessing and Tokenization, Language Models, Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis |
| BTOEE506X | Open Elective-I (e.g., Supply Chain Management) | Elective | 3 | Introduction to Supply Chain Management, Logistics and Transportation, Inventory Management, Procurement and Sourcing, Supply Chain Risk Management |
| BTAICL507 | Machine Learning Lab | Lab | 1 | Implementing Supervised Learning Models, Implementing Unsupervised Learning Models, Hyperparameter Tuning, Cross-Validation Techniques, Data Preprocessing and Feature Scaling |
| BTAICL508 | Artificial Intelligence Lab | Lab | 1 | Implementing Search Algorithms, Constraint Satisfaction Problems, Game Playing AI, Logical Reasoning Implementation, Planning Agents |
| BTAICL509 | Deep Learning Lab | Lab | 1 | Building Neural Networks, Image Classification with CNNs, Sequence Modeling with RNNs, Transfer Learning Applications, Deep Learning Model Deployment Basics |
| BTAIPR510 | Mini Project | Project | 2 | Problem Identification, Literature Survey, Design and Implementation, Testing and Evaluation, Project Report and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTHSC601 | Management II | Core | 3 | Financial Management, Marketing Management, Human Resource Management, Operations Management, Total Quality Management |
| BTAIC602 | Reinforcement Learning | Core | 3 | Markov Decision Processes (MDPs), Dynamic Programming in RL, Monte Carlo Methods, Temporal-Difference Learning (Q-learning, SARSA), Deep Reinforcement Learning |
| BTAIC603 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem, Spark Framework, MapReduce Paradigm, NoSQL Databases |
| BTAIC604 | Cloud Computing | Core | 3 | Cloud Computing Paradigms (IaaS, PaaS, SaaS), Virtualization Technology, Cloud Security Challenges, Cloud Deployment Models, Introduction to AWS/Azure/GCP Services |
| BTAIE605X | Departmental Elective-II (e.g., Predictive Analytics) | Elective | 3 | Time Series Analysis, Forecasting Techniques, Regression Modeling, Classification Trees, Prescriptive Analytics |
| BTOEE606X | Open Elective-II (e.g., Entrepreneurship Development) | Elective | 3 | Concept of Entrepreneurship, Business Idea Generation, Market Survey and Feasibility Study, Business Plan Formulation, Funding and Legal Aspects for Startups |
| BTAICL607 | Reinforcement Learning Lab | Lab | 1 | Implementing Q-learning, SARSA Algorithm, OpenAI Gym Environments, Policy Gradient Methods, Deep Q-Networks (DQN) |
| BTAICL608 | Big Data Analytics Lab | Lab | 1 | Hadoop Installation and Configuration, MapReduce Programming, Spark Data Processing, Hive and Pig Scripting, Data Ingestion with Flume/Sqoop |
| BTAICL609 | Cloud Computing Lab | Lab | 1 | Creating Virtual Machines, Deploying Web Applications on Cloud, Configuring Cloud Storage, Cloud Networking Basics, Introduction to Serverless Computing |
| BTAIPR610 | Minor Project | Project | 2 | Advanced Problem Definition, System Design and Architecture, Implementation and Integration, Testing, Debugging, and Optimization, Comprehensive Project Report and Demo |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAIC701 | Data Science and Engineering | Core | 3 | Data Pipelines and ETL, Feature Engineering and Selection, Model Deployment and Monitoring, Data Governance and Ethics, A/B Testing and Experiment Design |
| BTAIC702 | Image Processing and Computer Vision | Core | 3 | Image Fundamentals and Filtering, Image Segmentation, Feature Extraction and Description, Object Detection and Recognition, Applications of Computer Vision |
| BTAIE703X | Departmental Elective-III (e.g., Generative AI) | Elective | 3 | Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, Text-to-Image Generation, Applications in Content Creation |
| BTAIE704X | Departmental Elective-IV (e.g., IoT for AI) | Elective | 3 | IoT Architecture and Protocols, Sensors and Actuators, Edge Computing for AI, Data Analytics in IoT, Cloud Integration for IoT Data |
| BTOEE705X | Open Elective-III (e.g., Cyber Security) | Elective | 3 | Network Security Fundamentals, Cryptography and Encryption, Web Application Security, Malware and Attack Vectors, Security Policies and Management |
| BTAICL706 | Data Science and Engineering Lab | Lab | 1 | Building Data Pipelines, Feature Engineering for Real-world Data, Model Deployment using Flask/Streamlit, Data Visualization for Insights, Evaluating Data-driven Solutions |
| BTAICL707 | Image Processing and Computer Vision Lab | Lab | 1 | Basic Image Manipulation, Implementing Edge Detection, Object Detection using OpenCV, Image Segmentation Algorithms, Face Recognition Implementation |
| BTAIPR708 | Seminar | Project | 1 | Literature Review, Research Topic Selection, Technical Presentation Skills, Report Writing, Critical Analysis of Research Papers |
| BTAIPR709 | Industrial Training / Internship | Practical | 3 | Real-world Project Experience, Industry Best Practices, Teamwork and Collaboration, Problem-solving in Industrial Setting, Technical Report and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAIE801X | Departmental Elective-V (e.g., AI in Healthcare) | Elective | 3 | Medical Image Analysis, AI for Drug Discovery, Clinical Decision Support Systems, Electronic Health Record Analysis, Ethical Considerations in Healthcare AI |
| BTOEE802X | Open Elective-IV (e.g., Research Methodology) | Elective | 3 | Problem Formulation and Hypothesis, Data Collection Techniques, Statistical Analysis for Research, Technical Report Writing, Ethics and Plagiarism in Research |
| BTAIPR803 | Major Project | Project | 10 | Comprehensive Project Definition, Advanced System Design and Architecture, Large-scale Implementation and Testing, Performance Evaluation and Optimization, Project Thesis and Viva-Voce |
| BTAIPR804 | Comprehensive Viva-Voce | Viva | 2 | Overall Subject Knowledge Assessment, Technical Communication Skills, Problem Solving Abilities, Understanding of Industry Trends, Career Preparedness Evaluation |
