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B-TECH in Artificial Intelligence Machine Learning at Gyan Ganga College of Technology
Jabalpur, Madhya Pradesh
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About the Specialization
What is Artificial Intelligence & Machine Learning at Gyan Ganga College of Technology Jabalpur?
This Artificial Intelligence & Machine Learning program at Gyan Ganga College of Technology focuses on equipping students with advanced theoretical knowledge and practical skills in developing intelligent systems. With India''''s rapid digital transformation, there is immense demand for AI/ML professionals across various sectors, from healthcare to finance, making this program highly relevant for future-ready engineers.
Who Should Apply?
This program is ideal for ambitious fresh graduates seeking entry into the booming fields of AI and Machine Learning. It also caters to working professionals looking to upskill in cutting-edge technologies and career changers transitioning into data-driven roles. Candidates with a strong aptitude for mathematics, programming, and problem-solving will thrive in this challenging yet rewarding specialization.
Why Choose This Course?
Graduates of this program can expect promising career paths as AI Engineers, Machine Learning Scientists, Data Scientists, and AI/ML consultants in India. Entry-level salaries typically range from INR 4-8 LPA, growing significantly with experience. Opportunities abound in product development, research, and technical consulting across Indian IT giants and innovative startups, often aligning with certifications like Google AI or AWS Machine Learning.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Focus on strong foundations in C/C++ and Python. Regularly practice coding problems on platforms to build logical thinking and algorithm implementation skills crucial for AI/ML.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses on Data Structures
Career Connection
Essential for cracking technical rounds in placements and for implementing complex AI/ML algorithms efficiently.
Excel in Engineering Mathematics- (Semester 1-2)
Build a robust understanding of calculus, linear algebra, and probability. These mathematical concepts are the backbone of machine learning algorithms. Attend extra tutorial sessions and solve diverse problems.
Tools & Resources
Khan Academy, NPTEL for engineering math, MIT OpenCourseware
Career Connection
Crucial for understanding algorithm mechanics, optimizing models, and progressing to advanced research roles in AI.
Engage in Peer Learning Groups- (Semester 1-2)
Form small study groups to discuss complex topics, solve assignments collaboratively, and clarify doubts. Teach concepts to peers to solidify your own understanding.
Tools & Resources
WhatsApp groups, Google Meet for discussions, college library for collaborative study spaces
Career Connection
Develops teamwork, communication, and problem-solving skills vital for collaborative industry projects.
Intermediate Stage
Undertake Mini AI/ML Projects- (Semester 3-5)
Apply theoretical knowledge from Data Structures, OOP, and AI/ML courses by building small-scale projects using Python. Start with simple classification, regression, or NLP tasks.
Tools & Resources
Kaggle datasets, GitHub for project hosting, Jupyter Notebook, TensorFlow/PyTorch tutorials
Career Connection
Builds a portfolio for internships and demonstrates practical application of skills to potential employers.
Participate in Hackathons & Coding Competitions- (Semester 3-5)
Actively join college-level or national hackathons and coding contests. This hones problem-solving under pressure, teamwork, and introduces you to real-world challenges.
Tools & Resources
College coding clubs, Devfolio, HackerEarth, local industry-sponsored hackathons
Career Connection
Provides exposure, networking opportunities, and a platform to showcase skills, often leading to internship offers.
Seek Early Industry Exposure- (Semester 4-5)
Attend webinars, industry talks, and workshops related to AI/ML. Consider doing a short project-based internship or shadow an industry professional to understand workflow and trends.
Tools & Resources
LinkedIn Learning, NASSCOM events, local tech meetups, company career pages for virtual internships
Career Connection
Helps in clarifying career goals, understanding industry expectations, and building a professional network.
Advanced Stage
Focus on Specialized Skill Development- (Semester 6-7)
Deep dive into a specific AI/ML sub-field like Deep Learning, NLP, Computer Vision, or Reinforcement Learning, aligning with your career interests. Pursue advanced online certifications.
Tools & Resources
Coursera (DeepLearning.AI), Udemy, edX, NVIDIA DLI courses, research papers
Career Connection
Makes you a specialist, highly sought after for specific roles, and enhances your value proposition for higher salaries.
Complete a Capstone/Major Project with Industry Relevance- (Semester 7-8)
Work on a substantial, real-world AI/ML project, potentially in collaboration with an industry partner. Aim for a publishable paper or a functional prototype.
Tools & Resources
Collaboration with faculty, industry mentors, university incubation centers, open-source AI frameworks
Career Connection
The most significant resume builder, demonstrating advanced skills, research capability, and problem-solving for complex industry scenarios, crucial for high-tier placements.
Intensive Placement & Interview Preparation- (Semester 7-8)
Systematically prepare for technical interviews, aptitude tests, and HR rounds. Practice mock interviews, refine your resume, and build a strong LinkedIn profile. Focus on core AI/ML concepts and their applications.
Tools & Resources
InterviewBit, GeeksforGeeks interview section, mock interview platforms, college placement cell workshops
Career Connection
Directly leads to successful placements in reputable companies and securing desirable job roles upon graduation.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics and Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Technical Vocational subject. Minimum 45% marks (40% for reserved category) in the above subjects taken together. Admission based on JEE Main/State Entrance Examination.
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BT-101 | Engineering Physics | Core | 3 | Oscillations and Waves, Wave Optics, Quantum Mechanics, Solid State Physics, Lasers and Fiber Optics |
| BT-103 | Basic Electrical & Electronics Engineering | Core | 3 | DC & AC Circuits, Transformers, Semiconductor Diodes, Bipolar Junction Transistors, Digital Logic Gates |
| BT-105 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Vector Calculus, Ordinary Differential Equations |
| BT-108 | Computer Programming | Core | 3 | C Language Fundamentals, Control Structures, Functions and Arrays, Pointers and Structures, File Handling |
| BT-106 | Professional Communication | Core | 2 | Grammar and Vocabulary, Reading and Writing Skills, Presentation Techniques, Group Discussion, Interview Skills |
| BT-110 | Engineering Physics Lab | Lab | 1 | Spectrometer experiments, Young''''s modulus, PN Junction characteristics, Hall effect, Photoelectric effect |
| BT-112 | Basic Electrical & Electronics Engineering Lab | Lab | 1 | Circuit laws verification, Transformer characteristics, Diode and Transistor biasing, Rectifier circuits, Logic gate verification |
| BT-114 | Computer Programming Lab | Lab | 1 | C program debugging, Array and string manipulations, Function implementation, Pointers and dynamic memory, File operations |
| BT-116 | Professional Communication Lab | Lab | 1 | Language lab activities, Public speaking practice, Group discussion strategies, Resume building, Mock interviews |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BT-201 | Engineering Chemistry | Core | 3 | Water Technology, Fuels and Combustion, Polymers, Phase Rule, Electrochemistry and Corrosion |
| BT-203 | Basic Mechanical Engineering | Core | 3 | Thermodynamics Basics, IC Engines, Refrigeration and Air Conditioning, Power Transmission, Manufacturing Processes |
| BT-205 | Engineering Mathematics-II | Core | 4 | Matrices and Linear Algebra, Numerical Methods, Fourier Series, Laplace Transforms, Partial Differential Equations |
| BT-208 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Introduction to CAD |
| BT-206 | Environment & Ecology | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Solid Waste Management, Climate Change, Sustainable Development |
| BT-218 | Constitution of India | Mandatory Non-Credit | 0 | Preamble and Basic Features, Fundamental Rights and Duties, Directive Principles of State Policy, Union and State Legislature, Judiciary and Local Self-Government |
| BT-219 | NSS/NCC/Yoga | Mandatory Non-Credit | 0 | Community Service Principles, Discipline and Leadership, Physical Fitness, Yoga and Meditation, Environmental Awareness |
| BT-210 | Engineering Chemistry Lab | Lab | 1 | Water hardness determination, Viscosity measurement, pH metric titration, Conductometric titration, Calorific value of fuel |
| BT-212 | Basic Mechanical Engineering Workshop | Lab | 1 | Fitting shop practice, Carpentry shop practice, Welding shop practice, Foundry shop practice, Sheet metal shop practice |
| BT-214 | Engineering Graphics Lab | Lab | 1 | Manual drawing practice, Orthographic drawing exercises, Isometric drawing exercises, Sectional views practice, Basic CAD commands |
| BT-216 | Communication Skills Lab | Lab | 1 | Listening and speaking skills, Pronunciation practice, Public presentation, Interview techniques, Telephonic etiquette |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI-301 | Discrete Structure | Core | 3 | Set Theory and Relations, Functions and Mappings, Propositional and Predicate Logic, Graph Theory, Algebraic Structures |
| AI-302 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Traversal, Sorting and Searching Algorithms |
| AI-303 | Object-Oriented Programming | Core | 3 | Classes and Objects, Inheritance and Polymorphism, Abstraction and Encapsulation, Constructors and Destructors, Exception Handling and File I/O |
| AI-304 | Digital Electronics & Logic Design | Core | 3 | Number Systems and Boolean Algebra, Logic Gates and K-Maps, Combinational Circuits, Sequential Circuits (Flip-Flops, Counters), Memories and PLDs |
| AI-305 | Computer Organization & Architecture | Core | 3 | Basic Computer Organization, CPU Organization and Design, Memory System Hierarchy, Input/Output Organization, Pipelining and Parallel Processing |
| AI-302(L) | Data Structures Lab | Lab | 1 | Implementations of lists, Stack and Queue operations, Tree traversal algorithms, Graph algorithms, Sorting and searching implementations |
| AI-303(L) | Object-Oriented Programming Lab | Lab | 1 | Class and object programs, Inheritance and polymorphism, Abstract classes and interfaces, Exception handling programs, File I/O operations |
| AI-304(L) | Digital Electronics & Logic Design Lab | Lab | 1 | Logic gate verification, Combinational circuit design, Sequential circuit design, Flip-flop implementation, Encoder/decoder circuits |
| AI-306 | Computer Aided Engineering Graphics | Lab | 1 | 2D drafting using CAD software, 3D modeling techniques, Assembly drawing, Solid modeling basics, Rendering and visualization |
| AI-307 | Minor Project-I | Project | 1 | Problem identification, System design, Module development, Testing and debugging, Project report writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI-401 | Algorithms Design & Analysis | Core | 3 | Asymptotic Notations, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| AI-402 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Deadlocks, Memory Management, File Systems and I/O |
| AI-403 | Database Management Systems | Core | 3 | ER Model, Relational Model and Algebra, SQL Queries, Normalization, Transaction Management |
| AI-404 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines |
| AI-405 | Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms (Heuristic, Adversarial), Knowledge Representation, Expert Systems |
| AI-401(L) | Algorithms Design & Analysis Lab | Lab | 1 | Implementation of sorting algorithms, Dynamic programming solutions, Greedy algorithm problems, Graph traversal algorithms, Time complexity analysis |
| AI-402(L) | Operating Systems Lab | Lab | 1 | Shell programming, Process creation and management, CPU scheduling algorithms, Deadlock detection and prevention, Memory allocation strategies |
| AI-403(L) | Database Management Systems Lab | Lab | 1 | SQL DDL/DML commands, Advanced SQL queries, Trigger and stored procedure, Database design, Normalization examples |
| AI-406 | Python Programming Lab | Lab | 1 | Python basics and data types, Control flow and functions, Object-oriented programming in Python, NumPy and Pandas for data handling, Matplotlib for visualization |
| AI-407 | Minor Project-II | Project | 1 | Project planning and scheduling, Design and implementation phases, Testing and validation, Technical documentation, Presentation of results |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI-501 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Reinforcement Learning Basics, Model Evaluation and Validation, Ensemble Methods |
| AI-502 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Backpropagation and Optimization, Transfer Learning |
| AI-503 | Data Mining & Warehousing | Core | 3 | Data Preprocessing, Association Rule Mining, Classification Techniques, Clustering Algorithms, Data Warehouse Architecture |
| AI-504 | Computer Networks | Core | 3 | OSI and TCP/IP Models, Network Topologies, Routing Protocols, Congestion Control, Application Layer Protocols |
| AI-5xx | AI&ML Elective-I (e.g., Natural Language Processing) | Elective | 3 | Text Preprocessing, Language Models (N-grams), Part-of-Speech Tagging, Sentiment Analysis, Machine Translation |
| OE-5xx | Open Elective-I (e.g., Entrepreneurship Development) | Elective | 3 | Entrepreneurial Mindset, Business Idea Generation, Business Plan Development, Startup Funding, Marketing Strategies |
| AI-501(L) | Machine Learning Lab | Lab | 1 | Linear/Logistic Regression, Decision Trees and SVMs, K-Means Clustering, Scikit-learn usage, Model evaluation metrics |
| AI-502(L) | Deep Learning Lab | Lab | 1 | Building ANNs with Keras/TensorFlow, CNN for image classification, RNN for sequence data, Hyperparameter tuning, Transfer learning applications |
| AI-504(L) | Computer Networks Lab | Lab | 1 | Network configuration commands, Socket programming, Packet capturing (Wireshark), Network traffic analysis, Client-server communication |
| AI-507 | Project Based Learning / Industrial Training | Project | 2 | Real-world problem solving, Team collaboration, Industry standard practices, Technical documentation, Presentation skills |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI-601 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Bellman Equations, Q-Learning and SARSA, Policy Gradients, Deep Reinforcement Learning |
| AI-602 | Big Data Analytics | Core | 3 | Big Data Concepts, Hadoop Ecosystem (HDFS, MapReduce), Spark Framework, NoSQL Databases, Data Stream Processing |
| AI-603 | Cloud Computing for AI | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization, Cloud Platforms (AWS, Azure, GCP for AI), Serverless Computing, Containerization (Docker, Kubernetes) |
| AI-6xx | AI&ML Elective-II (e.g., Ethical AI) | Elective | 3 | AI Ethics Principles, Bias and Fairness in AI, Data Privacy and Security, Transparency and Explainability (XAI), Accountability in AI Systems |
| OE-6xx | Open Elective-II (e.g., Financial Management) | Elective | 3 | Capital Budgeting, Working Capital Management, Sources of Finance, Investment Decisions, Financial Markets |
| AI-602(L) | Big Data Analytics Lab | Lab | 1 | Hadoop installation and commands, MapReduce programming, Spark RDD operations, Hive queries, NoSQL database operations (e.g., MongoDB) |
| AI-603(L) | Cloud Computing for AI Lab | Lab | 1 | AWS/Azure/GCP setup for AI, Deploying ML models on cloud, Serverless functions (Lambda/Functions), Containerizing applications, Cloud storage solutions |
| AI-606 | Minor Project-III | Project | 2 | Advanced problem-solving, Innovative solution development, Prototype building, Cross-functional teamwork, Technical presentation |
| AI-607 | Seminar | Seminar | 1 | Literature review, Technical paper presentation, Public speaking, Q&A handling, Research methodology |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI-701 | Advanced Machine Learning | Core | 3 | Generative Models (GANs, VAEs), Bayesian Learning, Kernel Methods, Dimensionality Reduction Techniques, Ensemble Learning Advanced |
| AI-7xx | AI&ML Elective-III (e.g., Explainable AI) | Elective | 3 | Interpretability vs Explainability, LIME and SHAP techniques, Global vs Local Explanations, Causal Inference in AI, Ethical Implications of XAI |
| AI-7xx | AI&ML Elective-IV (e.g., Robotics & Automation) | Elective | 3 | Robot Kinematics and Dynamics, Robot Sensing and Actuation, Robot Control Architectures, Path Planning Algorithms, Industrial Automation Systems |
| OE-7xx | Open Elective-III (e.g., Project Management) | Elective | 3 | Project Life Cycle, Project Planning and Scheduling, Resource Management, Risk Management, Quality Assurance |
| AI-701(L) | Advanced Machine Learning Lab | Lab | 1 | Implementing GANs, Bayesian network inference, Kernel PCA applications, Advanced clustering techniques, Ensemble model fine-tuning |
| AI-705 | Major Project-I | Project | 6 | In-depth problem definition, System architecture design, Complex module development, Extensive testing and validation, Comprehensive technical report |
| AI-706 | Industrial Training / Internship | Practical | 3 | Industry work exposure, Application of academic knowledge, Professional skill development, Networking opportunities, Internship report and presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI-8xx | AI&ML Elective-V (e.g., Cyber Security & AI) | Elective | 3 | AI for Threat Detection, Anomaly Detection in Networks, Malware Analysis with ML, Secure AI System Design, Adversarial Attacks on AI |
| PE-8xx | Professional Elective (e.g., Advanced Database Systems) | Elective | 3 | Distributed Databases, NoSQL Databases (MongoDB, Cassandra), Data Lakes and Warehouses, Graph Databases, Database Security and Privacy |
| AI-803 | Major Project-II | Project | 10 | Project refinement and deployment, Performance optimization, Advanced research and innovation, Potential for publication/patent, Professional project defense |
| AI-804 | Comprehensive Viva | Viva | 2 | Overall subject knowledge, Problem-solving abilities, Communication skills, Technical understanding, Career aspirations |
