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BE in Name Artificial Intelligence And Machine Learning Seats 120 Average Tuition Fee Approximately Inr 3 12 000 Per Year Merit Quota Inr 20 00 000 1st Year Then Inr 10 00 000 2nd 4th Year Per Year Management Quota at RV College of Engineering
Bengaluru, Karnataka
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About the Specialization
What is {"name": "Artificial Intelligence and Machine Learning", "seats": 120, "average_tuition_fee": "Approximately INR 3,12,000 per year (merit quota); INR 20,00,000 (1st year) then INR 10,00,000 (2nd-4th year) per year (management quota)"} at RV College of Engineering Bengaluru?
This Artificial Intelligence and Machine Learning program at Rashtreeya Vidyalaya College of Engineering focuses on equipping students with advanced knowledge and practical skills in AI, ML, and Data Science. It emphasizes computational intelligence, data-driven decision-making, and intelligent system development crucial for India''''s rapidly expanding tech industry. The curriculum is designed to foster innovation and problem-solving capabilities, addressing the growing demand for AI specialists across various sectors in the Indian market.
Who Should Apply?
This program is ideal for aspiring engineers and innovators, including fresh 10+2 graduates with a strong aptitude for mathematics and computing, seeking entry into the high-growth fields of AI and ML. It also caters to graduates from allied engineering disciplines looking to specialize, or early-career professionals aiming to upskill and transition into roles focused on intelligent systems and data analytics within the Indian technology landscape.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths as AI engineers, Machine Learning specialists, Data Scientists, and Robotics engineers in top Indian tech companies, startups, and research institutions. Entry-level salaries typically range from INR 6-10 lakhs per annum, growing significantly with experience. The program aligns with industry certifications, providing a strong foundation for professional growth and leadership roles in India''''s AI-driven economy.
Student Success Practices
Foundation Stage
Master Core Programming & Data Structures- (Semester 1-2)
Focus diligently on understanding C/C++ fundamentals and mastering data structures like arrays, linked lists, trees, and graphs, alongside essential algorithms. This forms the bedrock for all advanced AI/ML concepts.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses on Data Structures & Algorithms
Career Connection
Strong DSA skills are non-negotiable for technical interviews at top product-based companies and AI/ML firms in India.
Build a Strong Mathematical Foundation- (Semester 1-2)
Pay close attention to Linear Algebra, Calculus, Probability, and Statistics. These mathematical pillars are fundamental to understanding how AI/ML algorithms work and are crucial for research and advanced development.
Tools & Resources
Khan Academy, NPTEL lectures, MIT OpenCourseWare (Mathematics for Computer Science), dedicated textbooks
Career Connection
Deep mathematical insight is vital for roles in algorithm development, research, and advanced machine learning engineering within the Indian tech sector.
Engage in Project-Based Learning & Peer Study- (Semester 1-2)
Form study groups and actively work on small programming projects. Collaborate with peers to solve problems, clarify concepts, and develop early teamwork skills. Attend college workshops on basic programming and tools.
Tools & Resources
GitHub for version control, local IDEs (VS Code, Code::Blocks), college computer labs
Career Connection
Practical project experience, even small ones, helps in building a portfolio and understanding real-world application, essential for securing early internships.
Intermediate Stage
Dive Deep into Core AI/ML Concepts with Practical Implementation- (Semester 3-5)
Beyond theory, actively implement machine learning algorithms from scratch using Python (NumPy, Pandas, Scikit-learn). Understand the nuances of different models (regression, classification, clustering) and their applications.
Tools & Resources
Kaggle for datasets and competitions, Coursera/edX specializations (e.g., Andrew Ng''''s Machine Learning), TensorFlow/PyTorch tutorials
Career Connection
Hands-on experience with ML frameworks and understanding model behavior is critical for becoming a competent Machine Learning Engineer or Data Scientist in India.
Develop Database and Web Development Skills- (Semester 3-4)
Master SQL for database management and gain proficiency in web technologies (HTML, CSS, JavaScript, a server-side framework). This allows you to build end-to-end applications that can integrate AI/ML models.
Tools & Resources
W3Schools, freeCodeCamp, MongoDB Atlas (for NoSQL exposure), local web servers (XAMPP, Node.js)
Career Connection
Full-stack capabilities with AI integration are highly valued in product development roles and by innovative startups across India.
Participate in Technical Competitions & Hackathons- (Semester 4-5)
Actively join college-level, inter-collegiate, and national hackathons or coding challenges focused on AI/ML. This provides exposure to real-world problems, teamwork dynamics, and rapid prototyping skills.
Tools & Resources
Devfolio, HackerEarth, Major League Hacking (MLH) events in India
Career Connection
Showcasing winning projects or even participation in competitive events significantly boosts your resume for internships and placements in the Indian tech industry.
Advanced Stage
Undertake Substantial Industry-Relevant Projects & Internships- (Semester 6-8)
Focus on securing internships with AI/ML teams in companies, or engage in significant capstone projects. Aim to solve complex problems, develop functional prototypes, and present tangible results. Seek faculty guidance for research papers or publications.
Tools & Resources
Company careers pages, LinkedIn, university placement cell, research databases (IEEE Xplore, ACM Digital Library)
Career Connection
Internships often lead to pre-placement offers, and strong project work is crucial for demonstrating applied skills to recruiters in India.
Specialize in Niche AI/ML Areas & Build a Portfolio- (Semester 6-7)
Based on electives and interest, delve deeper into areas like Deep Learning, NLP, Computer Vision, or Big Data. Develop a strong portfolio of projects, contributing to open-source or showcasing on GitHub/personal website.
Tools & Resources
Specialized online courses (e.g., fast.ai for Deep Learning), arXiv for latest research, GitHub Pages for portfolio hosting
Career Connection
A specialized portfolio demonstrates expertise and passion, making you a more attractive candidate for specific AI/ML roles and research opportunities in India.
Network Actively & Prepare for Placements- (Semester 7-8)
Attend industry seminars, workshops, and career fairs. Connect with alumni and professionals on LinkedIn. Start rigorous placement preparation early, including mock interviews (technical, HR, coding), resume building, and soft skills development.
Tools & Resources
LinkedIn, Glassdoor, interview preparation platforms (e.g., InterviewBit), college placement cell resources
Career Connection
Networking can open doors to opportunities, and thorough preparation is key to converting interviews into lucrative job offers from leading Indian tech companies.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 4 years (8 semesters)
Credits: 168 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BCE11 | Calculus and Differential Equations | Basic Science Course (BSC) | 4 | Differential Calculus, Integral Calculus, Partial Differentiation, Ordinary Differential Equations, Vector Calculus |
| 23BCE12 | Engineering Physics | Basic Science Course (BSC) | 4 | Modern Physics, Quantum Mechanics, Solid State Physics, Lasers and Fiber Optics, Superconductivity |
| 23BCE13 | Elements of Civil Engineering and Mechanics | Engineering Science Course (ESC) | 3 | Introduction to Civil Engineering, Engineering Mechanics, Building Materials, Surveying, Environmental Engineering |
| 23BCE14 | Basic Electrical Engineering | Engineering Science Course (ESC) | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines |
| 23BCE15 | Programming for Problem Solving | Engineering Science Course (ESC) | 3 | Introduction to C, Control Statements, Functions, Arrays, Pointers, Structures |
| 23BEL16 | Engineering Physics Lab | Basic Science Course (BSC) | 1 | Experimentation with Lasers, Optical Fibers, Semiconductor Devices, Magnetic Fields, Wave Phenomena |
| 23BEL17 | Basic Electrical Engineering Lab | Engineering Science Course (ESC) | 1 | Verification of Network Theorems, Measurement of Power, Characteristics of Diodes, Transistors, Rectifiers |
| 23BEL18 | Programming for Problem Solving Lab | Engineering Science Course (ESC) | 1 | C Programming exercises on Conditional Statements, Loops, Functions, Arrays, Strings, File Operations |
| 23BHS19 | Technical English / Communicative English | Humanities and Social Sciences including Management Courses (HSMC) | 1 | Grammar, Vocabulary, Reading Comprehension, Technical Writing, Presentation Skills |
| 23BHM110 | Professional Skills | Humanities and Social Sciences including Management Courses (HSMC) | 1 | Self-awareness, Goal Setting, Time Management, Stress Management, Interpersonal Skills |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BCE21 | Linear Algebra & Differential Equations | Basic Science Course (BSC) | 4 | Matrices, Determinants, Systems of Linear Equations, Eigenvalues, Vector Spaces, Differential Equations |
| 23BCE22 | Engineering Chemistry | Basic Science Course (BSC) | 4 | Electrochemistry, Corrosion, Water Technology, Fuels and Combustion, Polymer Chemistry, Nanomaterials |
| 23BCE23 | Computer Aided Engineering Graphics | Engineering Science Course (ESC) | 3 | Orthographic Projections, Isometric Projections, Sectional Views, AutoCAD Commands, Drafting Standards |
| 23BCE24 | Elements of Mechanical Engineering | Engineering Science Course (ESC) | 3 | Thermodynamics, IC Engines, Refrigeration, Power Transmission, Manufacturing Processes, Robotics |
| 23BCE25 | Data Structures | Engineering Science Course (ESC) | 3 | Arrays, Linked Lists, Stacks, Queues, Trees, Graphs, Searching, Sorting |
| 23BEL26 | Engineering Chemistry Lab | Basic Science Course (BSC) | 1 | Volumetric Analysis, pH-metry, Conductometry, Colorimetry, Synthesis of Polymers |
| 23BEL27 | Computer Aided Engineering Graphics Lab | Engineering Science Course (ESC) | 1 | Drawing Projections using CAD Software, Dimensioning, Assembly Drawings |
| 23BEL28 | Data Structures Lab | Engineering Science Course (ESC) | 1 | Implementation of Stacks, Queues, Linked Lists, Trees, Sorting Algorithms in C/C++ |
| 23BHS29 | Universal Human Values / Indian Constitution | Humanities and Social Sciences including Management Courses (HSMC) | 1 | Self-exploration, Human Values, Ethics, Indian Constitution, Fundamental Rights, Duties |
| 23BHM210 | Environmental Science and Sustainability | Humanities and Social Sciences including Management Courses (HSMC) | 1 | Ecosystems, Biodiversity, Pollution, Renewable Energy, Environmental Management, Sustainable Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIM31 | Data Structures & Algorithms | Professional Core Course (PCC) | 4 | Algorithm Analysis, Linked Lists, Stacks and Queues, Trees, Graphs, Sorting and Searching |
| 23AIM32 | Computer Organization & Architecture | Professional Core Course (PCC) | 4 | Digital Logic, Data Representation, CPU Design, Memory Hierarchy, I/O Organization, Pipelining |
| 23AIM33 | Discrete Mathematics & Graph Theory | Basic Science Course (BSC) | 4 | Set Theory, Logic and Proofs, Relations and Functions, Number Theory, Counting Techniques, Graph Theory |
| 23AIM34 | Database Management Systems | Professional Core Course (PCC) | 4 | Database Concepts, SQL, ER Modeling, Relational Algebra, Normalization, Transaction Management |
| 23AIM35 | Object-Oriented Programming with Python | Professional Core Course (PCC) | 3 | Python Fundamentals, OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Exception Handling, File I/O |
| 23AIML36 | Data Structures & Algorithms Lab | Professional Core Course (PCC) | 1 | Implementation of Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms, Algorithm Analysis Exercises |
| 23AIML37 | Database Management Systems Lab | Professional Core Course (PCC) | 1 | SQL Queries, Database Design, ER Diagrams, PL/SQL, Triggers and Stored Procedures |
| 23AIML38 | Object-Oriented Programming with Python Lab | Professional Core Course (PCC) | 1 | Python Programming Exercises on OOP, File Handling, Web Scraping Basics, Data Manipulation, GUI Development with Python |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIM41 | Design and Analysis of Algorithms | Professional Core Course (PCC) | 4 | Asymptotic Notations, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Backtracking, Branch and Bound |
| 23AIM42 | Operating Systems | Professional Core Course (PCC) | 4 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, I/O Systems |
| 23AIM43 | Probability & Statistics for AI | Basic Science Course (BSC) | 4 | Probability Theory, Random Variables, Probability Distributions, Hypothesis Testing, Regression Analysis, Correlation |
| 23AIM44 | Introduction to Artificial Intelligence | Professional Core Course (PCC) | 4 | AI Agents, Search Algorithms, Game Theory, Knowledge Representation, Logic Programming, Planning |
| 23AIM45 | Web Programming | Professional Core Course (PCC) | 3 | HTML, CSS, JavaScript, DOM Manipulation, AJAX, Server-side Scripting (PHP/Node.js), Database Connectivity, Web Security Basics |
| 23AIML46 | Operating Systems Lab | Professional Core Course (PCC) | 1 | Shell Scripting, Process Creation, Inter-Process Communication, CPU Scheduling Algorithms, Memory Allocation Techniques |
| 23AIML47 | Introduction to Artificial Intelligence Lab | Professional Core Course (PCC) | 1 | Implementation of Search Algorithms, Heuristic Functions, Logic Programming (Prolog), Constraint Satisfaction Problems |
| 23AIML48 | Web Programming Lab | Professional Core Course (PCC) | 1 | Dynamic Web Page Development, Client-side Scripting, Server-side Scripting, Database Integration, Responsive Design |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIM51 | Machine Learning | Professional Core Course (PCC) | 4 | Supervised Learning, Unsupervised Learning, Regression and Classification, Clustering Algorithms, Model Evaluation Metrics, Ensemble Methods |
| 23AIM52 | Theory of Computation | Professional Core Course (PCC) | 4 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Undecidability |
| 23AIM53 | Computer Networks | Professional Core Course (PCC) | 4 | OSI Model, TCP/IP Protocol Suite, Data Link Layer, Network Layer, Transport Layer, Application Layer |
| 23AIM54 | Research Methodology and IPR | Humanities and Social Sciences including Management Courses (HSMC) | 2 | Research Design, Data Collection and Analysis, Report Writing, Intellectual Property Rights, Patents and Copyrights, Research Ethics |
| 23AIME55 | Neural Networks and Deep Learning (Department Elective Course - I example) | Department Elective Course (DEC) | 3 | Perceptrons and Backpropagation, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformers, Generative Models, Deep Learning Frameworks |
| 23AIML56 | Machine Learning Lab | Professional Core Course (PCC) | 1 | Implementation of Regression Algorithms, Classification Algorithms, Clustering Algorithms, Feature Engineering, Model Evaluation, Scikit-learn/TensorFlow/PyTorch |
| 23AIML57 | Computer Networks Lab | Professional Core Course (PCC) | 1 | Network Configuration, Socket Programming, Protocol Implementation, Network Simulation Tools, Packet Analysis |
| 23AIMI58 | Mini Project | Professional Core Course (PCC) | 2 | Project Planning, Design and Implementation, Testing and Debugging, Documentation, Presentation Skills |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIM61 | Natural Language Processing | Professional Core Course (PCC) | 4 | Text Preprocessing, N-grams and Word Embeddings, POS Tagging, Named Entity Recognition, Sentiment Analysis, Machine Translation |
| 23AIM62 | Software Engineering | Professional Core Course (PCC) | 4 | Software Development Life Cycle, Requirements Engineering, Software Design Patterns, Software Testing, Maintenance, Project Management |
| 23AIM63 | Data Warehousing & Data Mining | Professional Core Course (PCC) | 4 | Data Warehouse Architecture, ETL Process, OLAP, Data Preprocessing, Association Rule Mining, Classification and Clustering |
| 23AIME64 | Image Processing and Computer Vision (Department Elective Course - II example) | Department Elective Course (DEC) | 3 | Image Fundamentals, Image Enhancement, Feature Extraction, Image Segmentation, Object Recognition, Machine Vision Applications |
| 23AIMOX | Department Open Elective Course - I | Open Elective Course (OEC) | 3 | Varies based on chosen inter-disciplinary elective (e.g., from other engineering branches, management, humanities) |
| 23AIML66 | Natural Language Processing Lab | Professional Core Course (PCC) | 1 | Implementation of NLP tasks, Tokenization and Stemming, Lemmatization, POS Tagging, Named Entity Recognition using NLTK/SpaCy |
| 23AIML67 | Software Engineering Lab | Professional Core Course (PCC) | 1 | UML Diagrams, Software Testing Tools, Version Control Systems, Project Management Tools, Agile Practices |
| 23AIM68 | Internship/Project Phase I | Professional Core Course (PCC) | 2 | Industry Exposure, Problem Definition, Literature Survey, Methodology Design, Initial Implementation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIM71 | Big Data Analytics | Professional Core Course (PCC) | 4 | Hadoop Ecosystem, MapReduce, HDFS, Apache Spark, NoSQL Databases, Stream Processing, Data Visualization |
| 23AIME72 | Speech Recognition and Synthesis (Department Elective Course - III example) | Department Elective Course (DEC) | 3 | Speech Production, Phonetics and Phonology, Acoustic Models, Language Models, Hidden Markov Models, Deep Learning for Speech, Text-to-Speech Systems |
| 23AIME76 | Explainable AI (Department Elective Course - IV example) | Department Elective Course (DEC) | 3 | Interpretability vs Explainability, Local and Global Explanations, LIME, SHAP, Causal Inference, Fairness and Bias in AI, Ethical AI |
| 23AIMOX | Department Open Elective Course - II | Open Elective Course (OEC) | 3 | Varies based on chosen inter-disciplinary elective (e.g., from other engineering branches, management, humanities) |
| 23AIM75 | Project Work Phase II | Professional Core Course (PCC) | 8 | Advanced Project Development, Research and Analysis, System Design and Implementation, Testing and Validation, Technical Documentation, Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIM81 | Professional Practice & Ethics | Humanities and Social Sciences including Management Courses (HSMC) | 2 | Professionalism, Code of Conduct, Ethical Dilemmas, Workplace Ethics, Social Responsibility, Legal Aspects of Engineering |
| 23AIM82 | Project Work Phase III | Professional Core Course (PCC) | 14 | Final Project Implementation, Advanced Research Contributions, System Integration, Performance Evaluation, Comprehensive Technical Report, Viva-Voce Examination |
