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B-TECH in Artificial Intelligence Machine Learning Ai Ml at Gandhi Institute For Technology
Khurda, Odisha
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About the Specialization
What is Artificial Intelligence & Machine Learning (AI&ML) at Gandhi Institute For Technology Khurda?
This Artificial Intelligence & Machine Learning (AI&ML) program at Gandhi Institute For Technology, Khurda, focuses on equipping students with deep knowledge and practical skills in AI algorithms, machine learning models, deep learning frameworks, and data science techniques. With India''''s rapid digital transformation, there is immense industry demand for AI&ML professionals across sectors like IT, finance, healthcare, and e-commerce, making this a highly relevant and forward-looking specialization.
Who Should Apply?
This program is ideal for fresh 10+2 graduates with a strong aptitude for mathematics and problem-solving, eager to delve into cutting-edge technologies. It also caters to aspiring data scientists, AI engineers, and machine learning specialists looking to build a solid foundational and advanced skill set. Individuals passionate about developing intelligent systems and data-driven solutions will find this program particularly rewarding.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths as AI Engineers, Machine Learning Developers, Data Scientists, and Research Analysts in top Indian and multinational companies. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning upwards of INR 15-30 LPA. The program also prepares students for advanced studies and professional certifications in AI/ML, fostering significant growth trajectories in the burgeoning Indian tech market.
Student Success Practices
Foundation Stage
Master Programming Fundamentals (Python & C)- (Semester 1-2)
Dedicate significant time to mastering core programming languages like C (for problem-solving logic) and Python (for AI/ML applications). Actively participate in coding contests and platforms to solidify algorithmic thinking.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, Jupyter Notebooks
Career Connection
Strong programming skills are non-negotiable for AI/ML roles, acting as the bedrock for implementing complex algorithms and solutions in future placements.
Build a Solid Mathematical Foundation- (Semester 1-3)
Focus intently on Discrete Mathematics, Linear Algebra, Probability, and Statistics. These are crucial for understanding the underlying principles of AI and Machine Learning algorithms. Join study groups to tackle complex problems.
Tools & Resources
Khan Academy, MIT OpenCourseWare (Mathematics), NPTEL lectures
Career Connection
A robust mathematical background enables deeper understanding of ML models, crucial for research roles, algorithm development, and advanced data science positions.
Engage in Technical Clubs and Workshops- (Semester 1-2)
Join the college''''s Computer Science or AI/ML clubs. Attend and actively participate in workshops on topics like Python programming, Git, or basic data science. This fosters peer learning and early exposure to practical skills.
Tools & Resources
College technical clubs, Local hackathons, Online programming tutorials
Career Connection
Early involvement builds a network, exposes students to real-world applications, and develops soft skills essential for team-based industry projects and interviews.
Intermediate Stage
Develop Practical AI/ML Projects- (Semester 3-5)
Start building small projects applying Machine Learning concepts (e.g., predicting house prices, image classification). Utilize open-source datasets and frameworks to get hands-on experience beyond lab exercises.
Tools & Resources
Kaggle, GitHub, Google Colab, Scikit-learn, TensorFlow/Keras
Career Connection
A portfolio of practical projects is vital for demonstrating applied skills to recruiters, significantly improving internship and placement prospects in AI/ML.
Seek Industry Exposure via Internships/Training- (Semester 4-6)
Actively search for summer internships or industrial training opportunities in AI/ML-focused startups or companies in cities like Bhubaneswar, Bengaluru, or Hyderabad. Even short-term virtual internships are valuable.
Tools & Resources
LinkedIn, Internshala, College placement cell, Company career pages
Career Connection
Internships provide invaluable real-world experience, build industry connections, and often lead to pre-placement offers, accelerating career entry.
Participate in AI/ML Competitions and Hackathons- (Semester 3-5)
Engage in national-level AI/ML hackathons or data science competitions on platforms like Kaggle, Analytics Vidhya, or regional university events. This enhances problem-solving skills and builds a competitive resume.
Tools & Resources
Kaggle Competitions, Analytics Vidhya, Major League Hacking
Career Connection
Winning or even participating in competitions showcases expertise, analytical prowess, and initiative, making candidates highly attractive to tech recruiters.
Advanced Stage
Specialize and Undertake Major Projects- (Semester 7-8)
Choose a specific area of AI (e.g., NLP, Computer Vision, Reinforcement Learning) for your major project. Focus on developing innovative solutions, perhaps involving research-level problems or real-world industrial challenges.
Tools & Resources
arXiv (for research papers), GitHub (for collaboration), Cloud platforms (AWS, Azure, GCP)
Career Connection
Deep specialization through a impactful major project positions you as an expert in a niche area, opening doors to specialized roles and research opportunities.
Prepare Rigorously for Placements- (Semester 6-8)
Begin placement preparation early by practicing aptitude tests, coding interviews (Data Structures & Algorithms), and mock technical interviews specifically for AI/ML roles. Polish your resume and LinkedIn profile, highlighting projects and skills.
Tools & Resources
LeetCode, Interviews/GeeksforGeeks, Mock interview platforms, Career Services Cell
Career Connection
Thorough preparation ensures success in the competitive campus placement drives, securing desired roles in leading tech companies.
Network Professionally and Seek Mentorship- (Semester 7-8)
Attend industry conferences, webinars, and workshops. Connect with alumni and professionals in the AI/ML field via LinkedIn. Seek out mentors who can guide your career path and provide insights into industry trends.
Tools & Resources
LinkedIn, Professional AI/ML communities, Alumni network events
Career Connection
Networking opens doors to hidden job opportunities, valuable career advice, and potential collaborations, crucial for long-term career growth and professional development.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Chemistry, and Mathematics (or equivalent), with minimum aggregate marks as per BPUT/OJEE/JEE Main guidelines for admission to B.Tech programs in Odisha.
Duration: 8 semesters / 4 years
Credits: 161 Credits
Assessment: Internal: 30% (Theory), 50% (Practical/Lab), External: 70% (Theory), 50% (Practical/Lab)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS-PH101 | Engineering Physics | Core | 3 | Oscillations and Waves, Optics, Quantum Mechanics, Solid State Physics, Lasers and Optical Fibers |
| BS-MA101 | Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Sequences and Series, Multivariable Calculus, Vector Calculus |
| ES-EE101 | Basic Electrical Engineering | Core | 4 | DC Circuits, AC Fundamentals, Three-phase AC Circuits, Electrical Machines, Electrical Safety |
| HS-HU101 | English | Humanities | 2 | Communication Skills, Grammar and Vocabulary, Written Communication, Oral Communication, Professional Ethics |
| ES-CS101 | Programming for Problem Solving | Core | 3 | C Programming Fundamentals, Data Types and Operators, Control Flow Statements, Functions and Pointers, Arrays and Structures |
| BS-PH191 | Engineering Physics Lab | Lab | 1.5 | Experiments on Optics, Mechanics, Electricity, Magnetism |
| ES-EE191 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Circuit Laws, Measurement of Electrical Quantities, Motor Characteristics |
| ES-ME191 | Engineering Graphics & Design | Lab | 2 | Engineering Drawing Standards, Orthographic Projections, Isometric Projections, Sectional Views, Introduction to CAD |
| ES-CS191 | Programming for Problem Solving Lab | Lab | 2 | C Program Implementation, Debugging Techniques, Problem-Solving using C |
| HS-HU191 | Language Lab | Lab | 1 | Phonetics and Pronunciation, Public Speaking, Group Discussion, Interview Skills |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS-CY101 | Engineering Chemistry | Core | 3 | Water Technology, Corrosion and Its Control, Polymers and Composites, Fuels and Combustion, Electrochemistry |
| BS-MA201 | Mathematics-II | Core | 4 | Linear Algebra, Ordinary Differential Equations, Laplace Transforms, Fourier Series, Probability and Statistics |
| ES-EC201 | Basic Electronics Engineering | Core | 3 | Diodes and Applications, Transistors, Amplifiers, Digital Electronics Fundamentals, Operational Amplifiers |
| ES-ME201 | Engineering Mechanics | Core | 3 | Statics of Particles and Rigid Bodies, Dynamics of Particles, Work-Energy Principle, Impulse and Momentum, Friction |
| ES-ME291 | Engineering Workshop | Lab | 1.5 | Carpentry, Fitting, Welding, Machining, Sheet Metal Operations |
| BS-CY191 | Engineering Chemistry Lab | Lab | 1.5 | Water Hardness Determination, Viscosity Measurement, Acid-Base Titrations, Instrumental Analysis |
| ES-EC291 | Basic Electronics Engineering Lab | Lab | 1 | Diode Characteristics, Rectifier Circuits, Transistor Amplifiers, Logic Gate Verification |
| HS-HU201 | Environmental Science | Humanities | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Sustainable Development Goals, Environmental Policies |
| PC-CS201 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Algorithms, Hashing Techniques |
| PC-CS291 | Data Structures Lab | Lab | 2 | Implementation of Data Structures, Algorithm Analysis, Problem Solving using Data Structures |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS-MA301 | Discrete Mathematics | Core | 4 | Set Theory and Logic, Relations and Functions, Graph Theory, Counting and Combinatorics, Algebraic Structures |
| PC-CS301 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis (Time/Space Complexity), Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms, NP-Completeness |
| PC-CS302 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks and Concurrency |
| PC-AI301 | Introduction to AI & Machine Learning | Core - AI&ML | 3 | Foundations of AI, Problem Solving by Search, Knowledge Representation, Machine Learning Basics, Supervised Learning Algorithms |
| ES-EC301 | Digital Electronics | Core | 3 | Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits (Flip-flops, Counters), Registers and Memories, Analog to Digital Converters |
| PC-CS391 | Design and Analysis of Algorithms Lab | Lab | 2 | Implementation of Sorting Algorithms, Graph Traversal Algorithms, Dynamic Programming Problems |
| PC-CS392 | Operating Systems Lab | Lab | 2 | Shell Scripting, Process Management Commands, Process Synchronization Problems, Memory Allocation Algorithms |
| PC-AI391 | Introduction to AI & ML Lab | Lab - AI&ML | 2 | Python for ML, Data Preprocessing, Implementing Supervised Learning Models, Using SciKit-Learn Library |
| MC-CS301 | Constitution of India | Mandatory Non-Credit | 0 | Framing of Indian Constitution, Fundamental Rights and Duties, Directive Principles of State Policy, Union and State Governments, Constitutional Amendments |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS-HU401 | Engineering Economics / Organisational Behaviour | Humanities | 3 | Basic Economic Principles, Cost Analysis, Project Evaluation Techniques, Organizational Behavior Concepts, Motivation and Leadership |
| PC-CS401 | Database Management Systems | Core | 3 | Relational Model, SQL Queries and Joins, ER Diagrams, Normalization, Transaction Management |
| PC-CS402 | Object-Oriented Programming | Core | 3 | OOP Concepts (Encapsulation, Inheritance, Polymorphism), Classes and Objects in Java/C++, Exception Handling, File I/O, GUI Programming Basics |
| PC-AI401 | Probability & Statistics for AI & ML | Core - AI&ML | 4 | Probability Theory, Random Variables and Distributions, Descriptive Statistics, Hypothesis Testing, Regression Analysis, Bayesian Inference |
| PC-AI402 | Artificial Neural Networks & Deep Learning | Core - AI&ML | 3 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow, Keras) |
| PC-CS491 | Database Management Systems Lab | Lab | 2 | SQL Practice, Database Design, PL/SQL Programming |
| PC-CS492 | Object-Oriented Programming Lab | Lab | 2 | Java/C++ Programming Exercises, Implementation of OOP Concepts, Developing Small OOP Projects |
| PC-AI491 | Artificial Neural Networks & Deep Learning Lab | Lab - AI&ML | 2 | Implementing ANNs from Scratch, Building CNNs for Image Classification, Experimenting with RNNs for Sequence Data, Using Keras and TensorFlow |
| MC-CS401 | Essence of Indian Traditional Knowledge | Mandatory Non-Credit | 0 | Vedas and Upanishads, Yoga and Ayurveda, Indian Philosophy, Traditional Art and Architecture, Sustainbility in Ancient India |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PC-AI501 | Natural Language Processing | Core - AI&ML | 3 | Text Preprocessing, N-grams and Language Models, Word Embeddings (Word2Vec, GloVe), POS Tagging and Named Entity Recognition, Text Classification and Sentiment Analysis |
| PC-AI502 | Computer Vision | Core - AI&ML | 3 | Image Processing Fundamentals, Feature Extraction (SIFT, HOG), Object Detection Algorithms, Image Segmentation, Deep Learning for Vision (CNNs) |
| PE-CS50X | Professional Elective-I | Professional Elective | 3 | Varies based on chosen elective (e.g., Data Warehousing, Cloud Computing, Distributed Systems), Data Modeling, ETL, Data Cubes, Data Mining Algorithms (for Data Warehousing), Cloud Service Models (IaaS, PaaS, SaaS), Virtualization, Cloud Security (for Cloud Computing) |
| OE-CS50X | Open Elective-I | Open Elective | 3 | Varies based on chosen elective (e.g., Python Programming, Cyber Security, Blockchain Fundamentals), Advanced Python features, Web Development with Python (for Python Programming), Network Security, Cryptography, Ethical Hacking (for Cyber Security) |
| PC-AI591 | Natural Language Processing Lab | Lab - AI&ML | 2 | Using NLTK and SpaCy libraries, Text Preprocessing and Tokenization, Building Language Models, Sentiment Analysis Implementation |
| PC-AI592 | Computer Vision Lab | Lab - AI&ML | 2 | OpenCV for Image Manipulation, Implementing Feature Detectors, Object Recognition Tasks, Image Filtering |
| PC-AI581 | Minor Project-I | Project | 3 | Problem Identification and Literature Survey, System Design and Architecture, Implementation and Testing, Project Reporting and Presentation |
| HS-HU501 | Professional Ethics & Human Values | Humanities | 2 | Ethical Theories, Professionalism and Responsibility, Cyber Ethics and Data Privacy, Environmental Ethics, Human Values in the Workplace |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PC-AI601 | Reinforcement Learning | Core - AI&ML | 3 | Markov Decision Processes (MDPs), Value and Policy Iteration, Q-Learning and SARSA, Deep Reinforcement Learning, Exploration vs Exploitation |
| PE-CS60X | Professional Elective-II | Professional Elective | 3 | Varies based on chosen elective (e.g., Big Data Analytics, Robotics, Quantum Computing), Hadoop, Spark, Data Stream Processing (for Big Data Analytics), Robot Kinematics, Motion Planning, Robot Control (for Robotics) |
| PE-CS60Y | Professional Elective-III | Professional Elective | 3 | Varies based on chosen elective (e.g., Computer Graphics, Advanced Database Systems, Soft Computing), Rendering, Shading, Animation, OpenGL (for Computer Graphics), Fuzzy Logic, Genetic Algorithms, Neural Networks (for Soft Computing) |
| OE-CS60Y | Open Elective-II | Open Elective | 3 | Varies based on chosen elective (e.g., Entrepreneurship Development, Financial Management, Disaster Management), Business Plan, Startup Ecosystem, Marketing Strategies (for Entrepreneurship), Risk Management, Emergency Planning, Post-Disaster Rehabilitation (for Disaster Management) |
| PC-AI691 | Reinforcement Learning Lab | Lab - AI&ML | 2 | OpenAI Gym Environment, Implementing Q-Learning and SARSA Agents, Policy Gradient Methods |
| PC-AI681 | Minor Project-II / Industrial Training | Project / Training | 3 | Advanced Project Development, Industrial Problem Solving, Report Writing and Presentation, Teamwork and Collaboration |
| PC-CS681 | Seminar | Seminar | 1 | Technical Literature Review, Research Paper Analysis, Public Speaking and Presentation Skills |
| MC-CS601 | Universal Human Values | Mandatory Non-Credit | 0 | Self-exploration and Self-awareness, Harmony in Human Relationships, Harmony in Society, Harmony in Nature and Existence, Professional Competence and Ethics |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PE-CS70X | Professional Elective-IV | Professional Elective | 3 | Varies based on chosen elective (e.g., IoT & Edge AI, Ethical Hacking, Distributed Systems), IoT Architecture, Edge Computing, AI at the Edge (for IoT & Edge AI), Penetration Testing, Vulnerability Assessment, Network Security (for Ethical Hacking) |
| PE-CS70Y | Professional Elective-V | Professional Elective | 3 | Varies based on chosen elective (e.g., Game AI, Robotics Process Automation, Digital Image Processing), AI for NPCs, Pathfinding, Decision Making (for Game AI), RPA Tools, Process Automation Design, Bots (for Robotics Process Automation) |
| OE-CS70Z | Open Elective-III | Open Elective | 3 | Varies based on chosen elective (e.g., Web Technologies, Mobile Application Development, Supply Chain Management), Frontend/Backend Development, APIs, Frameworks (for Web Technologies), Android/iOS Development, UI/UX Principles (for Mobile Application Development) |
| PC-AI781 | Major Project-I | Project | 4 | Advanced System Design, Implementation of Complex AI/ML Models, Rigorous Testing and Evaluation, Detailed Documentation and Interim Report |
| PC-AI782 | Internship / Industrial Training | Training | 3 | Real-world Industry Experience, Application of Academic Knowledge, Professional Skill Development, Industry-specific Projects |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PE-CS80X | Professional Elective-VI | Professional Elective | 3 | Varies based on chosen elective (e.g., Explainable AI, Generative AI, Data Privacy & Security), Interpretability Methods, Ethical AI, Fairness in ML (for Explainable AI), GANs, VAEs, Diffusion Models (for Generative AI) |
| OE-CS80Y | Open Elective-IV | Open Elective | 3 | Varies based on chosen elective (e.g., Project Management, IPR, Non-Conventional Energy Sources), Project Planning, Scheduling, Risk Management (for Project Management), Patents, Copyrights, Trademarks, Intellectual Property Law (for IPR) |
| PC-AI881 | Major Project-II | Project | 8 | Full-scale System Development and Deployment, Research and Innovation, Comprehensive Thesis Writing, Project Defense and Presentation |
| PC-AI882 | Comprehensive Viva Voce | Viva Voce | 2 | Overall Technical Knowledge Evaluation, Problem-Solving Skills Assessment, Communication and Presentation Skills |
