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B-TECH in Artificial Intelligence Machine Learning at Sagar Institute of Research & Technology
Bhopal, Madhya Pradesh
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About the Specialization
What is Artificial Intelligence & Machine Learning at Sagar Institute of Research & Technology Bhopal?
This Artificial Intelligence & Machine Learning program at Sagar Institute of Research & Technology, Bhopal, focuses on equipping students with advanced knowledge and practical skills in cutting-edge AI and ML technologies. Given India''''s burgeoning tech industry and increasing adoption of AI across sectors like healthcare, finance, and e-commerce, this specialization is highly relevant. The curriculum, designed by RGPV, emphasizes a strong foundation in computational mathematics, data science, and algorithm design to meet the evolving demands of the Indian and global markets.
Who Should Apply?
This program is ideal for ambitious fresh graduates holding a 10+2 qualification with a strong foundation in Physics, Chemistry, and Mathematics, seeking entry into the high-growth fields of AI and Machine Learning. It also caters to those with a keen analytical mind and an interest in problem-solving through data-driven approaches. Students aspiring to contribute to India''''s digital transformation and innovation ecosystem will find this curriculum particularly beneficial, preparing them for future innovations.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths as AI Engineers, Machine Learning Scientists, Data Scientists, NLP Specialists, and Computer Vision Engineers. Entry-level salaries in India typically range from INR 4-8 LPA, with experienced professionals potentially earning INR 15-30+ LPA in top-tier companies. The program prepares students for roles in startups, MNCs operating in India, and government research organizations, fostering strong growth trajectories and leadership opportunities in technology.
Student Success Practices
Foundation Stage
Master Core Programming and Mathematics- (Semester 1-2)
Dedicate significant time to strengthen foundational programming skills, primarily in C and Python, and build a robust understanding of Engineering Mathematics concepts. Consistent practice on online coding platforms is crucial.
Tools & Resources
HackerRank, GeeksforGeeks, LeetCode, NPTEL/SWAYAM courses for advanced math
Career Connection
A strong foundation in these areas is indispensable for clearing initial technical rounds in placements and internships, forming the bedrock for advanced AI/ML concepts.
Engage in Interdisciplinary Exploration- (Semester 1-2)
While focusing on core subjects, actively participate in workshops or introductory sessions related to basic electrical, electronics, and mechanical engineering. This broad exposure helps in understanding diverse AI applications.
Tools & Resources
College workshops, Online tutorials for Arduino/Raspberry Pi, Basic electronics kits
Career Connection
This interdisciplinary knowledge is beneficial for roles in areas like IoT, robotics, or embedded AI systems, providing a competitive edge in a multi-faceted industry.
Cultivate Effective Communication Skills- (Semester 1-2)
Actively participate in language labs, group discussions, and technical presentation exercises. Seek feedback to improve spoken and written English, which is vital for professional interactions.
Tools & Resources
College Language Lab, Toastmasters International (if available locally), Online English grammar resources
Career Connection
Excellent communication skills are critical for interview success, effective team collaboration, and clearly articulating project ideas to technical and non-technical audiences.
Intermediate Stage
Build an AI/ML Project Portfolio- (Semester 3-5)
Start developing mini-projects using Python and relevant libraries (NumPy, Pandas, Scikit-learn). Focus on applying learned algorithms to real-world datasets, even small ones, to demonstrate practical skills.
Tools & Resources
Kaggle (datasets and competitions), GitHub (for showcasing projects), Google Colab/Jupyter Notebooks
Career Connection
A strong project portfolio is a key differentiator during placements, providing tangible evidence of your ability to apply theoretical knowledge to solve real problems.
Participate in Coding and Data Science Competitions- (Semester 3-5)
Regularly engage in online coding contests and data science challenges on platforms like CodeChef, LeetCode, and Kaggle. This enhances problem-solving abilities and exposes you to diverse problem types.
Tools & Resources
CodeChef, LeetCode, Kaggle, HackerRank
Career Connection
Participation in competitions builds a strong technical profile, improves algorithmic thinking, and can lead to recognition, which is highly valued by recruiters in the tech industry.
Network and Seek Industry Mentorship- (Semester 3-5)
Attend industry webinars, tech talks, and local meetups (e.g., Data Science communities in Bhopal/Indore). Connect with professionals on LinkedIn to gain insights into industry trends, internship opportunities, and potential mentorship.
Tools & Resources
LinkedIn, Meetup.com (for local tech communities), Industry-specific online forums
Career Connection
Networking can open doors to internships, mentorship, and job opportunities that might not be publicly advertised, providing valuable career guidance and industry exposure.
Advanced Stage
Deep Dive into Specializations with Advanced Projects- (Semester 6-8)
Choose a specific niche within AI (e.g., NLP, Computer Vision, Reinforcement Learning) and undertake a comprehensive project, possibly your final year project. Utilize advanced frameworks like TensorFlow/PyTorch and potentially integrate cloud platforms.
Tools & Resources
TensorFlow/Keras, PyTorch, AWS/Azure/GCP free tier, OpenCV (for Computer Vision), NLTK/SpaCy (for NLP)
Career Connection
Developing a high-quality, specialized project demonstrates expertise and dedication, making you a strong candidate for advanced roles or research positions in your chosen AI domain.
Systematic Placement and Higher Education Preparation- (Semester 6-8)
Begin systematic preparation for campus placements, focusing on aptitude tests, technical interviews, and mock group discussions. Simultaneously, explore opportunities for M.Tech/Ph.D. in India (IITs, IISc) or abroad, and prepare for entrance exams like GATE or GRE.
Tools & Resources
Placement preparation books, Online mock interview platforms, Coaching for GATE/GRE, University websites for higher studies
Career Connection
Thorough preparation ensures you are competitive for both immediate job opportunities and future academic pursuits, aligning with your long-term career aspirations.
Focus on Ethical AI and Responsible Development- (Semester 6-8)
Integrate ethical considerations, bias detection, and fairness principles into all advanced AI projects. Understand the societal impact of AI and how to develop responsible AI solutions.
Tools & Resources
AI ethics guidelines (e.g., NITI Aayog), Responsible AI toolkits (e.g., IBM AI Fairness 360), Research papers on AI ethics
Career Connection
Demonstrating awareness and commitment to ethical AI development is increasingly valued by employers and is crucial for becoming a responsible and impactful AI professional in India and globally.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics and Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Technical Vocational subject. Obtained at least 45% marks (40% for reserved categories) in the above subjects taken together, as per DTE Madhya Pradesh norms.
Duration: 8 semesters / 4 years
Credits: 164 Credits
Assessment: Internal: 30% (for theory subjects), External: 70% (for theory subjects)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BT101 | Engineering Physics | Core | 4 | Quantum Physics, Optics, Solid State Physics, Lasers, Semiconductor Physics |
| BT102 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Matrices, Vector Calculus, Ordinary Differential Equations |
| BT103 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, Electrical Machines, Basic Power Systems |
| BT104 | Engineering Graphics & Design | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Auto-CAD Introduction |
| BT105 | Computer Programming | Core | 3 | C Language Basics, Data Types and Operators, Control Structures, Functions, Arrays and Pointers |
| BT106 | Engineering Physics Lab | Lab | 1 | Optics Experiments, Semiconductor Device Characteristics, Magnetic Properties Measurement, Measurement Techniques, Error Analysis |
| BT107 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Network Theorems, Measurement of Power, Study of Transformers, Motor Characteristics, Circuit Simulation |
| BT108 | Computer Programming Lab | Lab | 1 | C Language Programming Exercises, Conditional Statements, Loops and Functions, Arrays and Strings, Basic File Handling |
| BT109 | Engineering Workshop/Manufacturing Practices | Lab | 1 | Carpentry Shop, Fitting Shop, Welding Shop, Machining Processes, Foundry |
| BT110 | Environmental Science & Engineering | Audit | 0 | Ecosystems, Natural Resources, Pollution and Control, Environmental Protection Acts, Sustainable Development |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BT201 | Engineering Chemistry | Core | 4 | Water Technology, Fuels and Combustion, Corrosion and its Control, Engineering Materials, Electrochemistry |
| BT202 | Engineering Mathematics-II | Core | 4 | Multivariable Calculus, Laplace Transforms, Fourier Series, Partial Differential Equations, Complex Analysis |
| BT203 | Basic Mechanical Engineering | Core | 3 | Thermodynamics Basics, IC Engines, Power Plants, Refrigeration and Air Conditioning, Manufacturing Processes |
| BT204 | Basic Electronics Engineering | Core | 3 | Diodes and Applications, Transistors and Amplifiers, Operational Amplifiers, Digital Logic Gates, Basic Communication Systems |
| BT205 | Communication Skills | Core | 2 | Grammar and Vocabulary, Reading Comprehension, Writing Skills, Spoken English, Presentation Skills |
| BT206 | Engineering Chemistry Lab | Lab | 1 | Volumetric Analysis, Water Quality Testing, Spectrophotometry, Preparation of Polymers, Viscosity Measurements |
| BT207 | Basic Mechanical Engineering Lab | Lab | 1 | IC Engine Performance Test, Refrigeration Cycle Analysis, Material Testing, Steam Boiler Study, Pump Characteristics |
| BT208 | Basic Electronics Engineering Lab | Lab | 1 | Diode Characteristics, Transistor Biasing, Amplifier Circuits, Logic Gate Verification, Oscillator Circuits |
| BT209 | Language Lab | Lab | 1 | Group Discussions, Mock Interviews, Public Speaking Practice, Role-Playing, Phonetics and Pronunciation |
| BT210 | Constitution of India | Audit | 0 | Preamble and Fundamental Rights, Directive Principles of State Policy, Union Executive and Legislature, Judiciary in India, Constitutional Amendments |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI301 | Discrete Structure | Core | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Trees, Algebraic Structures |
| AI302 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Traversal, Sorting and Searching Algorithms |
| AI303 | Digital Electronics | Core | 3 | Number Systems and Codes, Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits, Memory Devices |
| AI304 | Object Oriented Programming | Core | 3 | OOP Concepts (Encapsulation, Inheritance), Classes and Objects, Polymorphism and Abstraction, Exception Handling, Templates and STL (C++) or Collections (Java) |
| AI305 | Introduction to AI & ML | Core | 3 | History and Foundations of AI, Intelligent Agents, Problem-Solving and Search Algorithms, Knowledge Representation, Introduction to Machine Learning |
| AI306 | Data Structures Lab | Lab | 1 | Implementation of Linked Lists, Stack and Queue Operations, Binary Search Tree Traversal, Graph Algorithms (BFS, DFS), Sorting and Searching Practice |
| AI307 | Digital Electronics Lab | Lab | 1 | Logic Gate Verification, Combinational Circuit Design, Flip-Flops and Latches, Counters and Registers, Multiplexers and Demultiplexers |
| AI308 | Object Oriented Programming Lab | Lab | 1 | Class and Object Implementation, Inheritance and Polymorphism Exercises, Abstract Classes and Interfaces, File I/O Operations, Basic GUI Programming (Optional) |
| AI309 | AI & ML Lab-I (Python Programming) | Lab | 1 | Python Fundamentals, Data Structures in Python, Functions and Modules, Numpy and Pandas Basics, Data Visualization with Matplotlib |
| BT3005 | Value Education | Audit | 0 | Ethics and Morality, Human Values, Professional Ethics, Corporate Social Responsibility, Universal Human Values |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI401 | Computer Architecture | Core | 3 | CPU Organization, Memory Hierarchy, I/O Organization, Instruction Set Architectures, Pipelining and Parallel Processing |
| AI402 | Design & Analysis of Algorithms | Core | 3 | Algorithm Analysis and Complexity, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms (BFS, DFS, Dijkstra) |
| AI403 | Operating System | Core | 3 | OS Functions and Types, Process Management, Memory Management, File Systems, I/O Systems and Deadlocks |
| AI404 | Database Management System | Core | 3 | Data Models (ER, Relational), Relational Algebra and Calculus, SQL Queries and Design, Normalization, Transaction Management and Concurrency Control |
| AI405 | Probability and Statistics for AI | Core | 3 | Probability Theory and Axioms, Random Variables and Distributions, Hypothesis Testing, Regression Analysis, Correlation and Covariance |
| AI406 | Computer Architecture Lab | Lab | 1 | Assembly Language Programming, CPU Simulation Tools, Memory Organization Simulation, I/O Operations, Pipelining Concepts |
| AI407 | Design & Analysis of Algorithms Lab | Lab | 1 | Implementation of Sorting Algorithms, Graph Traversal Algorithms, Dynamic Programming Solutions, Greedy Algorithm Implementations, Time and Space Complexity Analysis |
| AI408 | Operating System Lab | Lab | 1 | Linux Commands and Utilities, Shell Scripting, Process Management Commands, CPU Scheduling Algorithms, Memory Allocation Techniques |
| AI409 | Database Management System Lab | Lab | 1 | SQL Querying (DDL, DML, DCL), Database Schema Definition, Data Manipulation and Retrieval, Joins and Subqueries, Database Connectivity (e.g., Python with SQL) |
| BT4005 | Essence of Indian Traditional Knowledge | Audit | 0 | Indian Knowledge Systems, Yoga and Ayurveda, Traditional Arts and Crafts, Indian Philosophy, Traditional Sciences and Technologies |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI501 | Data Mining & Warehousing | Core | 3 | Data Preprocessing, Data Warehousing Concepts, Association Rule Mining, Classification Techniques, Clustering Algorithms, Big Data Introduction |
| AI502 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation and Validation, Ensemble Methods |
| AI503 | Natural Language Processing | Core | 3 | NLP Basics and Text Preprocessing, Language Models, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation, Text Summarization |
| AI504 | Computer Networks | Core | 3 | OSI and TCP/IP Models, Network Topologies and Devices, Routing Protocols, Transport Layer Protocols (TCP, UDP), Network Security Basics |
| AI505(A) | Digital Image Processing | Professional Elective – I (Example) | 3 | Image Fundamentals, Image Enhancement in Spatial Domain, Image Restoration, Image Segmentation, Color Image Processing, Wavelets and Multi-resolution Processing |
| AI506 | Data Mining & Warehousing Lab | Lab | 1 | Data Preprocessing using Python/R, Weka Tool for Data Mining, Implementation of Association Rules, Classification Algorithms Practice, Clustering Algorithm Experiments |
| AI507 | Machine Learning Lab | Lab | 1 | Implementation of Regression Models, Classification Algorithms (SVM, Decision Trees), Clustering (K-Means) Implementation, Model Evaluation Metrics, Scikit-learn Library Practice |
| AI508 | Natural Language Processing Lab | Lab | 1 | Text Preprocessing with NLTK, Tokenization and Stemming, POS Tagging and NER, Sentiment Analysis Implementation, Word Embeddings Introduction |
| AI509 | Computer Networks Lab | Lab | 1 | Network Configuration Commands, Socket Programming, Network Simulation Tools (e.g., Packet Tracer), TCP/UDP Protocol Implementation, Network Sniffing Tools |
| AI510 | AI & ML Mini Project-I | Project | 2 | Problem Identification, Literature Survey, Data Collection and Preprocessing, Model Development and Evaluation, Report Writing and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI601 | Deep Learning | Core | 3 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow/PyTorch), Generative Adversarial Networks (GANs) |
| AI602 | Cloud Computing for AI | Core | 3 | Cloud Architecture and Deployment Models, Service Models (IaaS, PaaS, SaaS), Virtualization, Cloud Security, AI Services on Cloud Platforms (AWS, Azure, GCP), Containerization (Docker, Kubernetes) |
| AI603 | Reinforcement Learning | Core | 3 | Markov Decision Processes (MDPs), Value and Policy Iteration, Q-Learning, SARSA Algorithm, Deep Reinforcement Learning, Actor-Critic Methods |
| AI604(A) | Big Data Analytics | Professional Elective – II (Example) | 3 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Programming, HDFS and YARN, Spark and its Components, NoSQL Databases |
| AI605(A) | Data Visualization | Open Elective – I (Example) | 3 | Principles of Data Visualization, Types of Charts and Graphs, Tools (Tableau, Power BI, D3.js), Interactive Dashboards, Storytelling with Data, Geospatial Visualization |
| AI606 | Deep Learning Lab | Lab | 1 | Implementation of CNNs for Image Classification, RNNs for Sequence Data, LSTM Networks, Transfer Learning Techniques, Hyperparameter Tuning |
| AI607 | Cloud Computing for AI Lab | Lab | 1 | Deploying ML Models on AWS/Azure/GCP, Using Cloud-based AI Services, Serverless Functions for AI, Containerizing AI Applications, Cloud Storage for Datasets |
| AI608 | Reinforcement Learning Lab | Lab | 1 | Implementation of Q-Learning, SARSA Algorithm Practice, OpenAI Gym Environments, Policy Gradient Methods, Solving Simple RL Problems |
| AI609 | AI & ML Mini Project-II | Project | 2 | Advanced Problem-Solving, Real-World Dataset Application, Model Optimization, Deployment Strategy, Technical Report and Presentation |
| AI610 | Seminar | Project | 1 | Technical Topic Research, Literature Review, Presentation Skills, Technical Report Writing, Critical Analysis |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI701 | Computer Vision | Core | 3 | Image Formation and Perception, Feature Extraction (SIFT, HOG), Object Detection and Recognition, Image Segmentation, Deep Learning for Computer Vision, Video Analysis |
| AI702 | Ethical AI | Core | 3 | Introduction to AI Ethics, Bias and Fairness in AI, Accountability and Transparency, Privacy and Data Protection, Societal Impact of AI, AI Regulations and Governance |
| AI703(A) | Internet of Things | Professional Elective – III (Example) | 3 | IoT Architecture, Sensors and Actuators, Communication Protocols (MQTT, CoAP), Edge and Cloud Computing for IoT, IoT Security and Privacy, IoT Application Development |
| AI704(A) | Speech Recognition | Professional Elective – IV (Example) | 3 | Speech Signal Processing, Acoustic Models, Language Models, Hidden Markov Models (HMMs), Deep Learning for Speech, Speech Synthesis |
| AI705(A) | Project Management | Open Elective – II (Example) | 3 | Project Lifecycle, Project Planning and Scheduling, Risk Management, Quality Management, Project Monitoring and Control, Agile Methodologies |
| AI706 | Computer Vision Lab | Lab | 1 | OpenCV Applications, Image Filtering and Edge Detection, Object Detection using Pre-trained Models, Facial Recognition, Image Segmentation Techniques |
| AI707 | AI & ML Project Phase – I / Industrial Training | Project/Internship | 6 | Problem Definition and Scope, Requirement Gathering, System Design, Initial Implementation and Testing, Technical Documentation, Industrial Exposure and Application |
| AI708 | Professional Practice | Practical | 1 | Technical Communication, Report Writing, Presentation Skills, Group Discussions, Professional Etiquette |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI801(A) | Robotics | Professional Elective – V (Example) | 3 | Robot Kinematics, Robot Dynamics, Sensors and Actuators in Robotics, Robot Control Systems, Path Planning and Navigation, Human-Robot Interaction |
| AI802(A) | Entrepreneurship Development | Open Elective – III (Example) | 3 | Business Idea Generation, Market Research and Analysis, Business Plan Development, Funding and Investment, Legal and Ethical Aspects of Business, Startup Ecosystem |
| AI803 | AI & ML Project Phase – II | Project | 10 | Advanced Research and Development, Large-Scale Implementation, Rigorous Testing and Validation, Performance Optimization, Comprehensive Project Report, Thesis Defense and Presentation |
| AI804 | Comprehensive Viva Voce | Viva | 2 | Overall Understanding of AI & ML Concepts, Interdisciplinary Knowledge, Problem-Solving Abilities, Critical Thinking and Application, Recent Advancements in AI/ML |
