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B-TECH in Artificial Intelligence Machine Learning at Vignana Bharathi Institute of Technology
Medchal-Malkajgiri, Telangana
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About the Specialization
What is Artificial Intelligence & Machine Learning at Vignana Bharathi Institute of Technology Medchal-Malkajgiri?
This B.Tech in Artificial Intelligence & Machine Learning program at Vignana Bharathi Institute of Technology focuses on equipping students with deep knowledge and practical skills in AI, ML, and their applications. With India''''s rapidly growing tech ecosystem, the program addresses the surging demand for skilled professionals in areas like data analytics, intelligent automation, and predictive modeling, preparing graduates to innovate in various industrial sectors.
Who Should Apply?
This program is ideal for aspiring engineers and innovators, particularly fresh 10+2 graduates with a strong aptitude for mathematics, programming, and problem-solving, seeking entry into the AI/ML domain. It also caters to those passionate about creating intelligent systems and contributing to cutting-edge technological advancements, providing a robust foundation for both academic and industrial careers.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including roles like Machine Learning Engineer, Data Scientist, AI Developer, and Business Intelligence Analyst. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning upwards of INR 15-30 LPA. The curriculum prepares students for industry certifications and provides a strong base for higher studies or entrepreneurship in the AI/ML space.
Student Success Practices
Foundation Stage
Master Programming & Problem Solving Fundamentals- (Semester 1-2)
Dedicate significant time to mastering C and Python programming, alongside data structures and algorithms. Participate in online coding challenges regularly to sharpen problem-solving skills and develop a logical approach to computational tasks.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, CodeChef, Python documentation
Career Connection
Strong programming fundamentals are the bedrock for all advanced AI/ML work and are crucial for cracking technical interviews for core engineering roles.
Build a Strong Mathematical Base- (Semester 1-2)
Focus intently on Linear Algebra, Calculus, Probability, and Statistics. These mathematical concepts are fundamental to understanding ML algorithms. Form study groups to tackle complex problems and utilize online resources for conceptual clarity.
Tools & Resources
Khan Academy, NPTEL courses, 3Blue1Brown (YouTube), standard textbooks
Career Connection
A solid mathematical understanding enables deeper comprehension of ML models, crucial for research, optimization, and developing novel algorithms, leading to roles in R&D or advanced data science.
Engage in Peer Learning & Early Project Exploration- (Semester 1-2)
Actively participate in class discussions and form peer study groups. Start exploring basic AI/ML concepts through mini-projects, even if simple. This hands-on approach helps consolidate theoretical knowledge and builds early practical confidence.
Tools & Resources
Kaggle (for beginner datasets), GitHub (for project collaboration), local college hackathons
Career Connection
Early exposure to project work and collaborative learning enhances teamwork skills and provides a portfolio for internships, showcasing initiative and practical application.
Intermediate Stage
Deep Dive into Core AI/ML & Database Concepts- (Semester 3-5)
Beyond coursework, explore advanced topics in Artificial Intelligence, Machine Learning, and Database Management Systems. Work on challenging projects that integrate these domains, focusing on practical implementation and performance optimization.
Tools & Resources
Coursera/edX specializations (e.g., Andrew Ng''''s ML course), TensorFlow/PyTorch tutorials, SQL practice platforms
Career Connection
This stage builds the core competencies directly sought by recruiters for AI/ML and data science roles, making students highly competitive for specialized internships.
Cultivate Problem-Solving through Data Challenges- (Semester 3-5)
Regularly participate in Kaggle competitions or similar data science challenges. This provides real-world data experience, allows experimentation with various algorithms, and helps refine problem-solving strategies under constraints.
Tools & Resources
Kaggle, DrivenData, Zindi
Career Connection
Winning or performing well in such competitions adds significant weight to a resume, demonstrating practical skill and a competitive edge, often leading to direct recruitment opportunities.
Network with Industry Professionals & Seek Internships- (Semester 3-5)
Attend webinars, workshops, and industry meetups to connect with professionals in AI/ML. Actively seek internships during summer breaks in relevant companies to gain hands-on industry exposure and understand real-world applications of learned concepts.
Tools & Resources
LinkedIn, industry-specific conferences (online/offline), college placement cell
Career Connection
Networking opens doors to mentorship, job referrals, and a clearer understanding of industry demands, significantly improving placement prospects.
Advanced Stage
Specialize through Advanced Electives & Research Projects- (Semester 6-8)
Choose professional and open electives strategically to specialize in areas like Deep Learning, NLP, Computer Vision, or Reinforcement Learning. Engage in substantial research projects, possibly culminating in a publication or a robust prototype.
Tools & Resources
Latest research papers (arXiv), specialized libraries (Hugging Face, OpenCV), academic conferences
Career Connection
Deep specialization makes graduates highly desirable for niche roles, R&D positions, and offers a strong foundation for postgraduate studies or entrepreneurial ventures.
Focus on Real-World AI Application & Deployment- (Semester 6-8)
Work on full-stack AI projects that involve not just model building but also deployment, maintenance, and ethical considerations. Understand MLOps principles and build projects that address genuine societal or industry problems.
Tools & Resources
Docker, Kubernetes, AWS/Azure/GCP ML services, Flask/Django for deployment
Career Connection
Demonstrating end-to-end project capabilities, including deployment, makes candidates valuable for roles in MLOps, AI product development, and solution architecture, leading to higher impact roles.
Sharpen Interview Skills & Build a Professional Portfolio- (Semester 6-8)
Actively prepare for technical and HR interviews, focusing on data structures, algorithms, ML concepts, and behavioral questions. Curate a strong online portfolio showcasing projects, contributions, and certifications. Attend mock interviews organized by the placement cell.
Tools & Resources
InterviewBit, LeetCode, LinkedIn, GitHub, personal website/blog
Career Connection
A well-prepared portfolio and strong interview performance are critical for securing top placements in leading tech companies and startups.
Program Structure and Curriculum
Eligibility:
- Intermediate (10+2) with Mathematics, Physics, Chemistry or equivalent, qualifying in TS EAMCET or JEE Main, as per TSCHE/AICTE guidelines.
Duration: 4 years / 8 semesters
Credits: 160 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS101 | Linear Algebra & Calculus | Core | 4 | Matrices, Eigen Values, Differential Calculus, Multiple Integrals, Vector Calculus |
| BS102 | Engineering Chemistry | Core | 3 | Water Treatment, Electrochemistry & Corrosion, Fuels and Combustion, Material Science, Polymers |
| HS101 | English | Core | 2 | Vocabulary Building, Grammar & Usage, Reading Comprehension, Writing Skills, Communication Skills |
| PC101 | Programming for Problem Solving | Core | 3 | C Programming Basics, Control Structures, Arrays and Strings, Functions and Pointers, Structures and File I/O |
| ES101 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines |
| ES151 | Engineering Chemistry Lab | Lab | 1.5 | Titrimetry, Water Analysis, Corrosion Experiments, Spectrophotometry, Polymer Testing |
| HS151 | English Language & Communication Skills Lab | Lab | 1.5 | Pronunciation Practice, Presentations, Group Discussions, Interview Skills, Role Play |
| PC151 | Programming for Problem Solving Lab | Lab | 1.5 | C Program Debugging, Conditional Statements, Loops and Arrays, Functions and Pointers, File Operations |
| ES152 | Basic Electrical Engineering Lab | Lab | 1.5 | Circuit Laws Verification, AC/DC Measurements, Motor Characteristics, Transformer Tests, Domestic Wiring |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS201 | Probability and Statistics | Core | 4 | Probability Distributions, Random Variables, Sampling Theory, Hypothesis Testing, Correlation and Regression |
| BS202 | Applied Physics | Core | 3 | Wave Optics, Lasers and Fiber Optics, Quantum Mechanics, Solid State Physics, Semiconductor Physics |
| ES201 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sections of Solids, Development of Surfaces, Introduction to Auto CAD |
| PC201 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Heaps, Graphs, Sorting and Searching Techniques |
| PC202 | Digital Logic Design | Core | 3 | Number Systems, Boolean Algebra, Combinational Circuits, Sequential Circuits, Memory & Programmable Logic |
| BS251 | Applied Physics Lab | Lab | 1.5 | Experiments on Optics, Laser Characteristics, Fiber Optics Testing, Semiconductor Devices, Photoelectric Effect |
| PC251 | Data Structures Lab | Lab | 1.5 | Array Implementations, Linked List Operations, Stack and Queue Applications, Tree Traversals, Graph Algorithms |
| PC252 | Python Programming Lab | Lab | 1.5 | Python Basics, Data Types and Control Flow, Functions and Modules, File Handling, Object-Oriented Programming in Python |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS301 | Discrete Mathematics | Core | 4 | Set Theory and Logic, Relations and Functions, Combinatorics, Graph Theory, Algebraic Structures |
| PC301 | Object Oriented Programming | Core | 3 | OOP Concepts (Java/C++), Classes and Objects, Inheritance and Polymorphism, Abstract Classes and Interfaces, Exception Handling |
| PC302 | Database Management Systems | Core | 3 | DBMS Architecture, Relational Model & SQL, Entity-Relationship Model, Normalization, Transactions & Concurrency Control |
| PC303 | Computer Organization & Architecture | Core | 3 | CPU Organization, Memory Hierarchy, Input/Output Organization, Instruction Set Architecture, Pipelining and Parallel Processing |
| PC304 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| PC351 | Object Oriented Programming Lab | Lab | 1.5 | Class and Object Implementation, Inheritance Scenarios, Polymorphism Usage, Interface Design, Exception Handling Practice |
| PC352 | Database Management Systems Lab | Lab | 1.5 | SQL Queries (DDL, DML), Join Operations, Subqueries and Views, PL/SQL Programming, Database Design |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS401 | Business Economics and Financial Analysis | Core | 3 | Demand and Supply Analysis, Production and Costs, Market Structures, Financial Accounting, Capital Budgeting |
| PC401 | Design and Analysis of Algorithms | Core | 3 | Asymptotic Notations, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| PC402 | Artificial Intelligence | Core | 3 | Introduction to AI, Problem-Solving through Search, Knowledge Representation, Logical Reasoning, Expert Systems |
| PC403 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms, Model Evaluation |
| PC404 | Formal Languages and Automata Theory | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines |
| PC451 | Artificial Intelligence Lab | Lab | 1.5 | Search Algorithm Implementation, Constraint Satisfaction Problems, Logic Programming (Prolog), Knowledge Representation Systems, Mini AI Project |
| PC452 | Machine Learning Lab | Lab | 1.5 | Linear Regression, Logistic Regression, SVM Implementation, Clustering Algorithms, Decision Trees |
| PC453 | Industry Oriented Mini Project / Internship | Project | 1.5 | Problem Definition, Literature Survey, System Design, Implementation, Report Writing |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PC501 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer, Network Layer, Transport and Application Layers |
| PC502 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Optimization Algorithms, Transfer Learning |
| PE501 | Data Warehousing and Mining | Professional Elective I | 3 | Data Warehouse Architecture, ETL Process, OLAP Operations, Data Mining Concepts, Association Rule Mining, Classification |
| OE501 | Renewable Energy Sources | Open Elective I | 3 | Solar Energy Systems, Wind Energy Conversion, Bio-energy, Hydro Power, Geothermal Energy |
| PC551 | Computer Networks Lab | Lab | 1.5 | Network Configuration, Socket Programming, Packet Analysis (Wireshark), Routing Protocols, Client-Server Applications |
| PC552 | Deep Learning Lab | Lab | 1.5 | Neural Network Implementation, CNNs for Image Classification, RNNs for Sequence Data, TensorFlow/PyTorch Basics, Hyperparameter Tuning |
| PC553 | Advanced English Language & Communication Skills Lab | Lab | 1.5 | Advanced Presentation Skills, Technical Report Writing, Interview Preparation, Negotiation Skills, Public Speaking |
| PC554 | Technical Seminar | Seminar | 1.5 | Research Methodology, Topic Selection, Presentation Skills, Technical Report Writing, Question and Answer Session |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS601 | Universal Human Values & Professional Ethics | Core | 3 | Human Values, Ethics and Morality, Professional Ethics, Ethical Dilemmas, Social Responsibility |
| PC601 | Natural Language Processing | Core | 3 | NLP Fundamentals, Text Preprocessing, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation |
| PC602 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Q-Learning and SARSA, Deep Reinforcement Learning |
| PE601 | Big Data Analytics | Professional Elective II | 3 | Big Data Concepts, Hadoop Ecosystem, MapReduce, Spark Framework, NoSQL Databases |
| OE601 | Intellectual Property Rights | Open Elective II | 3 | Patents and Copyrights, Trademarks, Industrial Designs, Geographical Indications, IPR Enforcement |
| PC651 | Natural Language Processing Lab | Lab | 1.5 | NLTK and SpaCy Usage, Text Preprocessing Tasks, Topic Modeling, Named Entity Recognition, Chatbot Development |
| PC652 | Reinforcement Learning Lab | Lab | 1.5 | Gridworld Problems, Q-Learning Implementation, SARSA Algorithm, OpenAI Gym Environments, Policy Gradient Methods |
| PC653 | AI & ML Project (Mini Project) | Project | 1.5 | Project Planning, Data Collection & Preprocessing, Model Development, Testing and Evaluation, Documentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PC701 | Computer Vision | Core | 3 | Image Processing Fundamentals, Feature Detection and Extraction, Image Segmentation, Object Recognition, Deep Learning for Vision |
| PE701 | Swarm Intelligence | Professional Elective III | 3 | Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Firefly Algorithm, Cuckoo Search Algorithm |
| PE702 | Pattern Recognition | Professional Elective IV | 3 | Statistical Pattern Recognition, Neural Networks for PR, Support Vector Machines, Clustering Techniques, Feature Extraction and Selection |
| OE701 | Entrepreneurship Development | Open Elective III | 3 | Entrepreneurial Process, Business Idea Generation, Business Plan Development, Funding Sources, Marketing and Legal Aspects |
| PC751 | Computer Vision Lab | Lab | 1.5 | OpenCV Library, Image Filtering, Edge Detection, Object Detection, Image Segmentation |
| PC752 | Project Phase-I | Project | 6 | Problem Identification, Literature Review, System Design, Initial Implementation, Mid-Term Report |
| PC753 | Technical Seminar-II | Seminar | 1.5 | Advanced Research Topics, Literature Synthesis, Effective Presentation Techniques, Critical Analysis, Technical Documentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PC801 | Ethics of AI & Data Science | Core | 3 | Ethical Principles in AI, Bias and Fairness in Algorithms, Privacy and Data Protection, Transparency and Explainability, Societal Impact of AI |
| PE801 | Explainable AI (XAI) | Professional Elective V | 3 | Interpretable Models, Feature Importance Methods, LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), Causal Inference in XAI |
| PC851 | Project Phase-II | Project | 10 | System Implementation and Development, Testing and Debugging, Performance Evaluation, Final Documentation, Project Demonstration and Viva |
| PC852 | Internship / Industry Oriented Project | Internship | 3 | Industry Problem Solving, Application of AI/ML Skills, Professional Communication, Teamwork and Collaboration, Internship Report |
