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B-TECH in Artificial Intelligence at Rashtrasant Tukadoji Maharaj Nagpur University
Nagpur, Maharashtra
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About the Specialization
What is Artificial Intelligence at Rashtrasant Tukadoji Maharaj Nagpur University Nagpur?
This B.Tech Artificial Intelligence program at Rashtrasant Tukadoji Maharaj Nagpur University focuses on providing a comprehensive understanding of AI, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotics, and Big Data. It is designed to equip students with theoretical knowledge and practical skills crucial for the rapidly evolving Indian technology landscape, catering to the growing demand for specialized AI professionals across various sectors.
Who Should Apply?
This program is ideal for fresh 10+2 graduates with a strong aptitude for mathematics, logical reasoning, and programming, aspiring to build careers in cutting-edge technology. It also caters to students keen on exploring data science, intelligent systems, automation, and those looking to contribute to India''''s digital transformation journey.
Why Choose This Course?
Graduates of this program can expect diverse and rewarding career paths such as AI Engineer, Machine Learning Scientist, Data Scientist, Robotics Engineer, or NLP Specialist. In the Indian market, entry-level salaries typically range from INR 4-8 LPA, while experienced professionals can command INR 10-25+ LPA, with growth trajectories leading to Lead AI Architect or Research Scientist roles in top Indian and global companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Develop a solid foundation in programming languages like Python and C++. Focus on understanding core concepts, data structures, and basic algorithms. Regularly practice coding problems on platforms to build logical thinking and problem-solving skills.
Tools & Resources
Python, C++, HackerRank, GeeksforGeeks, NPTEL courses
Career Connection
Strong programming skills are the bedrock for any IT role, especially in AI. This ensures readiness for technical interviews and efficient code development in future projects.
Build a Strong Mathematical Base- (Semester 1-3)
Dedicate significant effort to Engineering Mathematics, Discrete Mathematics, and Probability & Statistics. These subjects are critical for understanding complex AI/ML algorithms. Seek out supplementary online resources and participate in math clubs for deeper insight.
Tools & Resources
Khan Academy, MIT OpenCourseware (Calculus, Linear Algebra), Practice textbooks
Career Connection
A robust mathematical background is indispensable for comprehending AI models, optimizing algorithms, and excelling in roles requiring advanced analytical thinking.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups with classmates to discuss challenging topics, solve problems collaboratively, and prepare for exams. Teaching others reinforces your own understanding and exposes you to different perspectives.
Tools & Resources
Discord, Google Meet, University Library Study Rooms
Career Connection
Enhances communication skills, fosters teamwork, and helps clarify complex concepts, all valuable for collaborative project work in industry.
Intermediate Stage
Deep Dive into Core Computer Science Concepts- (Semester 3-5)
Focus on mastering Data Structures and Algorithms, Object-Oriented Programming, Operating Systems, and Database Management Systems. These form the architectural pillars for any AI application. Participate in competitive programming contests to hone skills.
Tools & Resources
LeetCode, CodeChef, Coursera (Algorithms Specialization), SQL Fiddle
Career Connection
These skills are heavily tested in technical interviews and are fundamental for designing efficient and scalable AI systems. Mastery ensures eligibility for core software development and AI roles.
Start Exploring AI/ML through Mini-Projects- (Semester 4-5)
Begin applying learned concepts by undertaking small AI/ML projects. This could involve simple prediction models, data analysis, or basic computer vision tasks. Utilize publicly available datasets to practice. Aim for 2-3 such projects.
Tools & Resources
Kaggle, Scikit-learn, Jupyter Notebook, TensorFlow/PyTorch basics
Career Connection
Practical application builds a project portfolio, demonstrates initiative, and helps solidify theoretical knowledge, making you a more attractive candidate for internships.
Participate in Technical Clubs and Workshops- (Semester 3-5)
Join university technical clubs (e.g., AI/ML Club, Coding Club) and attend workshops on emerging technologies. This exposes you to new tools, trends, and provides networking opportunities with peers and faculty mentors.
Tools & Resources
University''''s official student clubs, Tech fests, Online webinars from IITs/NPTEL
Career Connection
Expands knowledge beyond the curriculum, helps discover areas of interest, and builds a professional network valuable for referrals and career guidance.
Advanced Stage
Specialize through Advanced Projects and Research- (Semester 6-8)
Undertake major projects in specific AI domains like Deep Learning, NLP, or Robotics. Aim for projects that solve real-world problems or involve research. Consider publishing your work in conferences or journals, even at student level.
Tools & Resources
Google Scholar, arXiv, GitHub, Advanced ML/DL libraries
Career Connection
A strong project portfolio with specialization demonstrates expertise to potential employers and can lead to research positions or advanced R&D roles in industry.
Secure Relevant Internships & Industrial Training- (Semester 7 (especially summer break before Sem 7))
Actively seek and complete internships with AI/ML teams in startups or established companies. This provides invaluable industry experience, exposure to professional workflows, and helps build a strong professional network. Convert internships into full-time offers.
Tools & Resources
LinkedIn, Internshala, Company career pages, University Placement Cell
Career Connection
Internships are crucial for gaining practical experience, understanding industry demands, and often serve as direct pathways to full-time employment with competitive packages.
Focus on Placement Preparation and Interview Skills- (Semester 7-8)
Dedicate time to comprehensive placement preparation, including mock interviews, aptitude tests, and resume building workshops. Practice case studies relevant to AI roles and hone your communication skills for technical and HR rounds.
Tools & Resources
Online aptitude tests, Mock interview platforms, University Placement Cell training
Career Connection
Effective placement preparation significantly increases your chances of securing a desirable job offer from top companies, ensuring a smooth transition from academics to a professional career.
Program Structure and Curriculum
Eligibility:
- Candidates must have passed 10+2 (HSC or equivalent) with Physics and Mathematics as compulsory subjects, along with one of Chemistry/Biotechnology/Biology/Technical Vocational subject, securing a minimum of 45% marks (40% for reserved categories and PwD candidates from Maharashtra State). Additionally, candidates must have appeared for MHT-CET or JEE (Main) as per DTE Maharashtra admission guidelines.
Duration: 4 years (8 semesters)
Credits: 167 Credits
Assessment: Internal: 20% (for Theory), 50% (for Practical/Tutorial), External: 80% (for Theory), 50% (for Practical/Tutorial)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS101T | Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Ordinary Differential Equations, Partial Differential Equations, Vector Calculus |
| BS102T | Engineering Chemistry | Core | 3 | Water Technology, Corrosion and its Control, Fuels and Lubricants, Polymers, Engineering Materials |
| ES101T | Basic Civil Engineering & Engineering Mechanics | Core | 3 | Introduction to Civil Engineering, Building Materials, Surveying, Forces and Equilibrium, Friction and Kinematics |
| ES103T | Engineering Graphics | Core | 2 | Introduction to Engineering Graphics, Orthographic Projections, Isometric Projections, Sectional Views, AutoCAD Basics |
| ES104T | Programming Language - I (Python) | Core | 3 | Python Fundamentals, Data Types and Operators, Control Structures, Functions and Modules, File Handling |
| HS101T | Professional Communication | Core | 2 | Basics of Communication, Verbal and Non-verbal Communication, Presentation Skills, Report Writing, Group Discussions |
| BS102P | Engineering Chemistry Lab | Lab | 1 | Volumetric Analysis, Instrumental Methods, Corrosion Experiments, Polymer Synthesis, Fuel Analysis |
| ES101P | Basic Civil Engineering & Engineering Mechanics Lab | Lab | 1 | Material Testing, Truss Analysis, Friction Experiments, Surveying Instruments, Moment of Inertia |
| ES104P | Programming Language - I (Python) Lab | Lab | 1 | Basic Python Programs, Conditional Statements, Looping Constructs, Function Implementation, File Operations |
| ES105P | Workshop Practice | Lab | 1 | Fitting Shop, Carpentry Shop, Welding Shop, Sheet Metal Shop, Foundry Shop |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS201T | Mathematics-II | Core | 4 | Linear Algebra, Multiple Integrals, Vector Calculus, Complex Analysis, Laplace Transforms |
| BS202T | Engineering Physics | Core | 3 | Quantum Mechanics, Wave Optics, Solid State Physics, Fiber Optics, Lasers |
| ES201T | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, Single-phase & Three-phase Systems |
| ES204T | Programming Language - II (C++) | Core | 3 | C++ Basics, Classes and Objects, Inheritance and Polymorphism, Templates and Exception Handling, File I/O in C++ |
| BS202P | Engineering Physics Lab | Lab | 1 | Optical Experiments, Semiconductor Devices, Magnetic Field Measurement, Resonance Circuits, Quantum Phenomena |
| ES201P | Basic Electrical Engineering Lab | Lab | 1 | Circuit Laws Verification, Transformer Characteristics, Motor Control, Power Measurement, AC Circuit Analysis |
| ES204P | Programming Language - II (C++) Lab | Lab | 1 | Object-Oriented Programming, Data Structures in C++, Algorithm Implementation, Standard Template Library, Debugging Techniques |
| MC201T | Environmental Studies | Audit | 0 | Natural Resources, Ecosystems, Environmental Pollution, Social Issues and Environment, Environmental Protection |
| MC202T | Value Education & Human Rights | Audit | 0 | Introduction to Value Education, Human Values, Ethics in Professional Life, Human Rights, Social Justice and Constitutional Values |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS301T | Engineering Mathematics-III | Core | 4 | Laplace Transform, Fourier Series, Probability and Statistics, Vector Spaces, Numerical Methods |
| PC301T | Data Structures & Algorithms | Core | 3 | Arrays, Stacks, Queues, Linked Lists, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| PC302T | Object-Oriented Programming | Core | 3 | Classes and Objects, Inheritance, Polymorphism, Abstract Classes and Interfaces, Exception Handling |
| PC303T | Digital Circuits & Fundamentals of Microprocessors | Core | 3 | Logic Gates and Boolean Algebra, Combinational Circuits, Sequential Circuits, Memory Organization, Microprocessor Architecture |
| PC304T | Discrete Mathematics | Core | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Counting Principles, Recurrence Relations |
| PC301P | Data Structures & Algorithms Lab | Lab | 1 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| PC302P | Object-Oriented Programming Lab | Lab | 1 | Class and Object Design, Inheritance and Polymorphism Implementation, Operator Overloading, File Handling in C++, Exception Handling Practice |
| PC303P | Digital Circuits & Microprocessors Lab | Lab | 1 | Logic Gate Implementation, Flip-Flops and Counters, Registers, 8085 Microprocessor Programming, Interfacing Techniques |
| PC305P | Software Development Lab-I | Lab | 1 | Version Control with Git, IDE Usage, Debugging Techniques, Basic UI Development, Software Documentation |
| MC301T | Indian Constitution | Audit | 0 | Preamble and Fundamental Rights, Directive Principles, Union and State Legislature, Judiciary, Constitutional Amendments |
| MC302T | Skill Development-I (Audit) | Audit | 0 | Soft Skills, Communication Enhancement, Time Management, Problem-Solving, Teamwork |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PC401T | Analysis of Algorithms | Core | 3 | Asymptotic Notations, Divide and Conquer, Dynamic Programming, Greedy Algorithms, NP-Completeness |
| PC402T | Operating System | Core | 3 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks and Concurrency |
| PC403T | Database Management System | Core | 3 | ER Model and Relational Model, SQL and Relational Algebra, Normalization, Transaction Management, Concurrency Control |
| PC404T | Computer Organization & Architecture | Core | 3 | CPU Organization, Pipelining, Memory Hierarchy, I/O Organization, Control Unit Design |
| HS401T | Professional Ethics & Cyber Law | Core | 2 | Ethical Theories, Cybercrime and Cyber Law, Intellectual Property Rights, Data Privacy and Security, Professional Accountability |
| PC401P | Analysis of Algorithms Lab | Lab | 1 | Sorting Algorithm Analysis, Graph Algorithm Implementation, Dynamic Programming Problems, Greedy Algorithm Problems, Time and Space Complexity Measurement |
| PC402P | Operating System Lab | Lab | 1 | Process Management Commands, CPU Scheduling Algorithms, Memory Allocation Algorithms, Deadlock Detection, File System Operations |
| PC403P | Database Management System Lab | Lab | 1 | SQL Queries, Database Design, ER Diagram Implementation, Normalization, Transaction Management |
| PC405P | Software Development Lab-II | Lab | 1 | Advanced UI/UX Design, Web Frameworks (Basic), API Integration, Software Testing Fundamentals, Deployment Basics |
| MC401T | Skill Development-II (Audit) | Audit | 0 | Analytical Thinking, Presentation Skills, Interview Preparation, Leadership Qualities, Conflict Resolution |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PC501T | Computer Networks | Core | 3 | OSI and TCP/IP Models, Network Topologies, Routing Protocols, Congestion Control, Application Layer Protocols |
| PC502T | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| PC503T | Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving and Search, Knowledge Representation, Uncertainty and Reasoning, Machine Learning Fundamentals |
| PC504T | Data Mining & Warehousing | Core | 3 | Data Preprocessing, Data Warehouse Architecture, OLAP Operations, Association Rule Mining, Classification and Clustering |
| PE501T | Big Data Analytics | Elective | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Spark Framework, NoSQL Databases, Big Data Challenges |
| OE501T | Open Elective-I | Elective | 3 | |
| PC501P | Computer Networks Lab | Lab | 1 | Socket Programming, Network Simulation Tools, Packet Analysis (Wireshark), Routing Protocol Configuration, Client-Server Applications |
| PC502P | Compiler Design Lab | Lab | 1 | Lexical Analyzer Implementation, Parser Implementation, Syntax Directed Translation, Intermediate Code Generation, Symbol Table Management |
| PC503P | Artificial Intelligence Lab | Lab | 1 | Python for AI, Search Algorithms (DFS, BFS), Constraint Satisfaction Problems, Logic Programming (Prolog basics), Simple Expert Systems |
| PC504P | Data Mining & Warehousing Lab | Lab | 1 | Data Preprocessing using Tools, OLAP Cube Operations, Association Rule Mining, Classification Algorithms, Clustering Algorithms |
| PW501P | Mini Project | Project | 2 | Project Planning, Requirement Analysis, Design and Implementation, Testing and Debugging, Report Writing and Presentation |
| MC501T | Skill Development-III (Audit) | Audit | 0 | Advanced Communication, Entrepreneurial Skills, Emotional Intelligence, Critical Thinking, Digital Literacy |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PC601T | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning Basics, Model Evaluation and Selection, Ensemble Methods |
| PC602T | Information Security | Core | 3 | Cryptography and Ciphers, Network Security (Firewalls, IDS), Web Security, Cyber Forensics Basics, Security Management |
| PC603T | Deep Learning | Core | 3 | Neural Network Fundamentals, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Deep Learning Frameworks (TensorFlow/PyTorch) |
| PE601T | Natural Language Processing | Elective | 3 | Text Preprocessing, Word Embeddings, POS Tagging and Parsing, Named Entity Recognition, Sentiment Analysis |
| OE601T | Open Elective-II | Elective | 3 | |
| PC601P | Machine Learning Lab | Lab | 1 | Linear and Logistic Regression, SVM and Decision Trees, Clustering Algorithms (K-Means), Dimensionality Reduction (PCA), Model Evaluation Metrics |
| PC602P | Information Security Lab | Lab | 1 | Cryptography Tools, Network Scanning, Vulnerability Assessment, Firewall Configuration, Intrusion Detection Systems |
| PC603P | Deep Learning Lab | Lab | 1 | Neural Network Implementation, CNN for Image Classification, RNN for Sequence Data, Transfer Learning, TensorFlow/Keras/PyTorch Practice |
| PW601P | Project Based Learning | Project | 2 | Real-world Problem Identification, Project Design, Implementation and Testing, Collaborative Development, Project Presentation |
| MC601T | Skill Development-IV (Audit) | Audit | 0 | Data Analysis Tools, Research Methodology, Technical Writing, Project Management Tools, Intellectual Property Basics |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PC701T | Reinforcement Learning | Core | 3 | Markov Decision Processes (MDPs), Value and Policy Iteration, Q-Learning and SARSA, Deep Reinforcement Learning, Actor-Critic Methods |
| PC702T | Big Data Analytics | Core | 3 | Hadoop Ecosystem, Spark for Big Data, NoSQL Databases, Stream Processing, Big Data Visualization |
| PE701T | Computer Vision | Elective | 3 | Image Processing Fundamentals, Feature Detection and Extraction, Object Recognition, Image Segmentation, Deep Learning for Vision |
| PE702T | Cyber Forensics & Ethical Hacking | Elective | 3 | Ethical Hacking Stages, Footprinting and Scanning, System Hacking, Malware Analysis, Web Application Attacks |
| OE701T | Open Elective-III | Elective | 3 | |
| PC701P | Reinforcement Learning Lab | Lab | 1 | OpenAI Gym Environments, Q-Learning Implementation, SARSA Implementation, Deep Q-Networks, Policy Gradient Methods |
| PC702P | Big Data Analytics Lab | Lab | 1 | Hadoop Installation and Configuration, MapReduce Programming, Spark Data Processing, NoSQL Database Operations, Data Visualization Tools |
| PW701P | Project | Project | 4 | Advanced Project Planning, System Design and Architecture, Implementation with Modern Technologies, Extensive Testing and Evaluation, Technical Report and Defense |
| PW702P | Internship / Industrial Training | Project | 2 | Industry Exposure, Practical Skill Application, Professional Networking, Problem Solving in Real-world, Internship Report |
| MC701T | Skill Development-V (Audit) | Audit | 0 | AI Ethics and Governance, Entrepreneurship in AI, Patent Filing Basics, Leadership in Tech, Global Business Communication |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PC801T | AI in IoT | Core | 3 | IoT Architecture and Protocols, Edge AI Computing, Sensor Data Analytics, Machine Learning for IoT Devices, Security and Privacy in AIoT |
| PC802T | Intelligent Robotics | Core | 3 | Robot Kinematics and Dynamics, Robot Sensors and Actuators, Motion Planning, Robot Vision, Machine Learning for Robotics |
| PC803T | Natural Language Processing | Core | 3 | Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), Sequence Models (RNN, LSTM, Transformer), Named Entity Recognition, Sentiment Analysis and Text Generation |
| OE801T | Open Elective-IV | Elective | 3 | |
| PW801P | Project | Project | 8 | Real-world AI Application Development, Research and Innovation, Comprehensive System Design, Extensive Testing and Optimization, Publication and Presentation |
| MC801T | Skill Development-VI (Audit) | Audit | 0 | Interview and GD Preparation, Resume Building, Company Specific Training, Startup Incubation, Professional Networking |
