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B-E-ARTIFICIAL-INTELLIGENCE-MACHINE-LEARNING in General at Vivekananda Institute of Technology
Bengaluru, Karnataka
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About the Specialization
What is General at Vivekananda Institute of Technology Bengaluru?
This B.E. Artificial Intelligence & Machine Learning program at Vivekananda Institute of Technology, affiliated with VTU, provides a robust foundation in AI/ML principles and practical applications. Tailored for India''''s burgeoning tech sector, it emphasizes problem-solving with cutting-edge technologies. The curriculum comprehensively covers foundational mathematics to advanced deep learning, preparing graduates for impactful roles in this dynamic field.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics, programming, and logical reasoning, seeking entry into the AI/ML domain. It also caters to working professionals aiming to upskill and transition into AI roles. Career changers from related engineering fields, possessing foundational computer science understanding, will find this program beneficial for transitioning into India''''s high-growth AI industry.
Why Choose This Course?
Graduates can expect diverse career paths in India, including AI Engineer, ML Scientist, Data Scientist, or NLP Specialist. Entry-level salaries range from INR 4-8 LPA, growing significantly with experience. The program aligns with industry certifications, fostering continuous professional development and strong growth trajectories within Indian tech giants, startups, and research institutions for impactful contributions.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice C and Python programming concepts, data structures, and algorithms through online coding platforms and college labs. Focus on logical problem-solving and clean code.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, NPTEL courses
Career Connection
Strong programming fundamentals are critical for internships and entry-level roles in AI/ML, forming the bedrock for complex algorithm implementation and debugging.
Build a Strong Mathematical Base- (Semester 1-2)
Dedicate time to understanding calculus, linear algebra, and probability concepts through rigorous study, solving diverse problems, and utilizing supplementary online resources.
Tools & Resources
Khan Academy, NPTEL lectures (e.g., Mathematics for ML), Open-source textbooks
Career Connection
A solid mathematical understanding is essential for comprehending the underlying principles of AI/ML algorithms, model optimization, and future research opportunities.
Engage in Peer Learning & Tech Clubs- (Semester 1-2)
Join college tech clubs focused on AI/ML and form study groups to discuss concepts, solve problems collaboratively, and participate in introductory workshops and hackathons.
Tools & Resources
College tech clubs, Discord/WhatsApp study groups, Open-source learning communities
Career Connection
Develops teamwork, communication, and exposure to diverse problem-solving approaches, all crucial for collaborative industry projects and professional networking.
Intermediate Stage
Undertake Mini-Projects & Hackathons- (Semester 3-5)
Apply learned concepts in data structures, Java, Python, and AI/ML basics by developing mini-projects independently or with teams, and actively participating in hackathons.
Tools & Resources
Kaggle datasets, GitHub, Google Colab, Online project idea platforms (e.g., Dev.to)
Career Connection
Builds a practical project portfolio, demonstrates problem-solving abilities, and provides experience for technical interviews and project-based roles.
Explore Specialization Tracks & Electives- (Semester 5)
Proactively research and select professional and open electives that align with personal interests in areas like Data Science, Cloud Computing, or specific AI applications.
Tools & Resources
Course catalogs, Industry trend reports, Career counseling, LinkedIn profiles
Career Connection
Allows for early specialization, making students more attractive to specific industry roles and providing a competitive edge in the job market.
Network with Industry Professionals- (Semester 4-5)
Attend webinars, industry talks, and workshops organized by the department or professional bodies; connect with alumni and professionals on platforms like LinkedIn.
Tools & Resources
LinkedIn, Professional networking events, Alumni meetups, Industry conferences
Career Connection
Opens doors for internships, mentorship, and job opportunities, providing invaluable insights into industry expectations and emerging trends.
Advanced Stage
Secure & Maximize Internship Experience- (Semester 6-8)
Actively seek and secure internships in reputable companies, applying core AI/ML knowledge to real-world problems and contributing meaningfully to industry projects.
Tools & Resources
College placement cell, Internshala.com, LinkedIn Jobs, Company career pages
Career Connection
Converts theoretical knowledge into practical skills, often leading to pre-placement offers (PPOs) and provides crucial industry exposure for resume building.
Develop a Capstone Project with Impact- (Semester 7-8)
Work on a substantial final year project (Phase 1 & 2) that addresses a real-world problem, potentially using advanced AI/ML techniques and showcasing innovation.
Tools & Resources
Research papers, Faculty mentorship, Industry collaboration, TensorFlow/PyTorch
Career Connection
A strong project acts as a differentiator, demonstrating advanced technical skills, problem-solving capabilities, and potential for innovation to prospective employers.
Prepare Holistically for Placements & Higher Studies- (Semester 7-8)
Engage in mock interviews, aptitude test preparation, refine resume/portfolio, and prepare for competitive exams (GATE, GRE) if considering postgraduate education.
Tools & Resources
Placement cell workshops, Online aptitude platforms, Interview prep guides, Alumni network
Career Connection
Ensures readiness for the job market, significantly increasing chances of securing desirable placements, or successful admission to postgraduate programs.
Program Structure and Curriculum
Eligibility:
- Pass in 10+2/PUC or equivalent examination with English as one of the languages and obtained a minimum of 45% of marks in aggregate in Physics, Mathematics, and any one of the following subjects: Chemistry, Biology, Biotechnology, Computer Science, Electronics (40% for SC/ST/OBC category candidates of Karnataka state).
Duration: 8 semesters / 4 years
Credits: 156 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MAT11 | Calculus and Differential Equations | Core | 4 | Differential Calculus, Integral Calculus, Ordinary Differential Equations, Laplace Transform, Applications of Laplace Transform |
| 22PHY12 | Engineering Physics | Core | 4 | Quantum Mechanics, Laser and Applications, Fiber Optics, Material Science, Nanotechnology |
| 22CIV13 | Civil Engineering and Engineering Mechanics | Core | 3 | Introduction to Civil Engineering, Introduction to Mechanics, Engineering Materials, Force Systems, Truss Analysis |
| 22ELE14 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Three Phase Systems, Electrical Machines, Measuring Instruments |
| 22CPL15 | Programming for Problem Solving | Core | 3 | Introduction to C, Control Structures, Functions, Arrays and Strings, Structures and Pointers |
| 22EGDL16 | Engineering Graphics and Design Lab | Lab | 2 | Introduction to Engineering Graphics, Orthographic Projections, Sectional Views, Isometric Projections, Development of Surfaces |
| 22PCDL17 | C Programming for Problem Solving Lab | Lab | 2 | C Program Development, Conditional Statements and Loops, Function Implementation, Array and String Operations, Pointers and Structures Applications |
| 22LCL18 | Language and Communication Lab | Lab | 1 | Basic English Grammar, Written Communication Skills, Oral Communication Skills, Presentation Techniques, Group Discussion |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MAT21 | Advanced Calculus and Numerical Methods | Core | 4 | Vector Calculus, Partial Differential Equations, Numerical Methods for Equations, Numerical Methods for Interpolation, Numerical Methods for Integration |
| 22CHE22 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion and its Control, Materials Chemistry, Fuels and Combustion, Environmental Chemistry |
| 22BCE23 | Basic Computer Engineering | Core | 3 | Computer System Basics, CPU Organization, Memory System, Input/Output Organization, Computer Networking Fundamentals |
| 22ME24 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics, IC Engines, Refrigeration and Air Conditioning, Power Transmission, Material Science |
| 22CPH25 | Python Programming for Problem Solving | Core | 3 | Python Fundamentals, Data Structures in Python, Functions and Modules, Object-Oriented Programming in Python, File Handling and Exceptions |
| 22BCLA26 | Basic Computer Engineering Lab | Lab | 2 | Linux Commands, Basic System Utilities, Assembly Language Basics, Network Configuration, Operating System Commands |
| 22PPL27 | Python Programming for Problem Solving Lab | Lab | 2 | Python Syntax and Control Flow, Conditional Logic and Loops, Function Definitions and Calls, List, Tuple, Dictionary Operations, Object-Oriented Programming Examples |
| 22KSD28 / 22KVE28 | Kannada (Ability Enhancement) / Constitution of India and Professional Ethics | Mandatory Non-credit | 0 | Kannada Language Skills / Indian Constitution, Fundamental Rights and Duties / Professional Ethics, State and Central Government / Cyber Law, Panchayat Raj / Human Rights, Electoral Process / Environmental Protection |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS31 | Mathematical Foundations for Computing | Core | 3 | Logic and Proofs, Set Theory and Relations, Functions and Sequences, Number Theory, Graph Theory |
| 22CS32 | Data Structures and Applications | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Trees, Graphs and Graph Traversal, Hashing Techniques |
| 22AIM33 | Discrete Mathematics and Graph Theory | Core | 3 | Set Theory, Relations and Functions, Permutations and Combinations, Probability, Graph Theory Fundamentals |
| 22AIM34 | Object Oriented Programming with Java | Core | 3 | Classes and Objects, Inheritance and Polymorphism, Interfaces and Packages, Exception Handling, Multithreading |
| 22AIM35 | Computer Organization and Architecture | Core | 3 | Basic Structure of Computers, Processor Organization, Memory System, Input/Output Organization, Pipelining and Parallel Processing |
| 22AIM36 | Data Structures Laboratory | Lab | 1 | Array and Linked List Operations, Stack and Queue Implementations, Tree Traversal Algorithms, Graph Representation and Traversals, Sorting and Searching Algorithms |
| 22AIM37 | Object Oriented Programming with Java Laboratory | Lab | 1 | Class and Object Creation, Inheritance and Method Overriding, Polymorphism and Interface Implementation, Exception Handling Scenarios, Multithreading Applications |
| 22KVK38 / 22KVE38 | Vyavaharika Kannada (Ability Enhancement) / Constitution of India and Professional Ethics | Mandatory Non-credit | 0 | Practical Kannada Communication / Indian Constitutional Principles, Cultural Aspects / Fundamental Rights and Duties, Administrative Structure / Professional Ethics, Simple Conversations / Role of Judiciary, Basic Vocabulary / Cyber Laws and Society |
| 22CPM39 | Technical Skill Enhancement Course | Skill | 1 | Communication Skills, Teamwork and Collaboration, Problem-Solving Strategies, Presentation Techniques, Career Planning |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS41 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis Techniques, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| 22CS42 | Operating Systems | Core | 4 | Introduction to Operating Systems, Process Management, CPU Scheduling, Memory Management, File Systems |
| 22AIM43 | Database Management Systems | Core | 3 | Database Concepts and Architecture, ER Model, Relational Algebra and Calculus, SQL Queries and Advanced SQL, Normalization and Transaction Management |
| 22AIM44 | Principles of Artificial Intelligence | Core | 3 | Introduction to AI Agents, Problem Solving by Search, Heuristic Search Techniques, Knowledge Representation and Reasoning, Introduction to Machine Learning |
| 22AIM45 | Introduction to Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms, Model Evaluation and Validation |
| 22AIM46 | DBMS Laboratory with Mini Project | Lab | 1 | SQL Querying and Data Definition, Database Design and Implementation, PL/SQL Programming, Transaction Management Concepts, Mini Project Development |
| 22AIM47 | AI-ML Laboratory | Lab | 1 | Python for AI/ML, Implementation of Search Algorithms, Regression Model Implementation, Classification Model Implementation, Clustering Algorithms |
| 22AIM48 | Environmental Studies | Mandatory Non-credit | 0 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Global Environmental Issues, Environmental Legislation and Ethics |
| 22CPM49 | Technical Skill Enhancement Course | Skill | 1 | Critical Thinking and Problem Solving, Report Writing, Professional Ethics and Values, Interview Preparation, Presentation Skills Development |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIM51 | Finite Automata and Compiler Design | Core | 4 | Finite Automata, Regular Expressions, Context-Free Grammars, Lexical Analysis, Parsing Techniques |
| 22AIM52 | Computer Networks | Core | 4 | Network Models (OSI/TCP-IP), Physical Layer, Data Link Layer, Network Layer, Transport and Application Layer |
| 22AIM53 | Probability and Statistics for AI/ML | Core | 3 | Probability Theory, Random Variables and Distributions, Joint Probability Distributions, Statistical Inference, Hypothesis Testing |
| 22AIM54X | Professional Elective – 1 | Elective | 3 | Choice from: Web Technologies, Advanced Java Programming, Data Warehousing and Data Mining, Specific topics depend on chosen elective |
| 22AIM541 | Web Technologies (Professional Elective – 1) | Elective | 3 | HTML5 and CSS3, JavaScript and DOM, Server-side Scripting, Database Connectivity, Web Security Fundamentals |
| 22AIM542 | Advanced Java Programming (Professional Elective – 1) | Elective | 3 | JDBC and Database Interaction, Servlets and JSP, Enterprise JavaBeans, Spring Framework Basics, Hibernate ORM |
| 22AIM543 | Data Warehousing and Data Mining (Professional Elective – 1) | Elective | 3 | Data Warehouse Architecture, OLAP Operations, Data Preprocessing, Association Rule Mining, Classification and Clustering Techniques |
| 22AIM55X | Open Elective – 1 | Elective | 3 | Choice from: Introduction to Data Science, Python for Data Science, Blockchain Technology, Specific topics depend on chosen elective |
| 22AIM551 | Introduction to Data Science (Open Elective – 1) | Elective | 3 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Data Visualization, Basic Machine Learning Models |
| 22AIM552 | Python for Data Science (Open Elective – 1) | Elective | 3 | Python Basics for Data Science, NumPy for Numerical Computing, Pandas for Data Manipulation, Matplotlib and Seaborn for Visualization, Introduction to Scikit-learn |
| 22AIM553 | Blockchain Technology (Open Elective – 1) | Elective | 3 | Cryptography Fundamentals, Distributed Ledger Technology, Bitcoin Blockchain, Ethereum and Smart Contracts, Consensus Mechanisms |
| 22AIM56 | AI-ML Project Based Learning | Lab | 2 | Problem Definition and Scoping, Data Collection and Preprocessing, Model Selection and Training, Evaluation and Optimization, Project Report and Presentation |
| 22AIM57 | Professional Skill Development | Skill | 1 | Resume Building, Interview Skills, Public Speaking, Leadership and Teamwork, Professional Etiquette |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIM61 | Deep Learning | Core | 4 | Neural Network Fundamentals, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Architectures and Applications |
| 22AIM62 | Natural Language Processing | Core | 4 | Text Preprocessing and Tokenization, N-grams and Language Models, Word Embeddings (Word2Vec, GloVe), Part-of-Speech Tagging and Parsing, Text Classification and Sentiment Analysis |
| 22AIM63 | Big Data Analytics | Core | 3 | Big Data Concepts and Challenges, Hadoop Ecosystem (HDFS, MapReduce), Spark for Big Data Processing, Data Stream Processing, NoSQL Databases |
| 22AIM64X | Professional Elective – 2 | Elective | 3 | Choice from: Cloud Computing, Internet of Things, Computer Vision, Specific topics depend on chosen elective |
| 22AIM641 | Cloud Computing (Professional Elective – 2) | Elective | 3 | Cloud Computing Models (IaaS, PaaS, SaaS), Virtualization Technology, Cloud Platforms (AWS, Azure basics), Cloud Security and Data Privacy, Serverless Computing |
| 22AIM642 | Internet of Things (Professional Elective – 2) | Elective | 3 | IoT Architecture and Protocols, Sensors, Actuators, and Devices, Communication Technologies (Wi-Fi, Zigbee, LoRa), IoT Data Analytics, IoT Security and Privacy |
| 22AIM643 | Computer Vision (Professional Elective – 2) | Elective | 3 | Image Processing Fundamentals, Feature Extraction and Description, Object Detection, Image Segmentation, Deep Learning for Computer Vision |
| 22AIM65X | Open Elective – 2 | Elective | 3 | Choice from: AI for Business, Web Scraping & Data Collection, Cyber Security Fundamentals, Specific topics depend on chosen elective |
| 22AIM651 | AI for Business (Open Elective – 2) | Elective | 3 | AI Applications in Business, AI Strategy and Transformation, Ethical AI in Business, ROI of AI Projects, Case Studies in AI Adoption |
| 22AIM652 | Web Scraping & Data Collection (Open Elective – 2) | Elective | 3 | HTTP and Web Fundamentals, HTML Parsing (BeautifulSoup), Web Crawling with Scrapy, Working with APIs for Data, Data Storage and Ethics of Scraping |
| 22AIM653 | Cyber Security Fundamentals (Open Elective – 2) | Elective | 3 | Network Security Basics, Cryptography Principles, Malware and Attack Types, Security Policies and Controls, Cybersecurity Best Practices |
| 22AIM66 | Deep Learning Laboratory | Lab | 2 | TensorFlow/PyTorch Implementation, CNN Model Development, RNN/LSTM Model Development, Transfer Learning Techniques, Deep Learning Model Deployment |
| 22AIM67 | Internship | Skill | 1 | Industry Exposure, Project Execution and Management, Report Writing and Documentation, Presentation Skills, Professional Work Ethics |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIM71 | Reinforcement Learning | Core | 4 | Markov Decision Processes, Dynamic Programming (Value & Policy Iteration), Monte Carlo Methods, Temporal Difference Learning (Q-Learning, SARSA), Deep Reinforcement Learning |
| 22AIM72 | Research Methodology and IPR | Core | 3 | Research Design and Problem Formulation, Data Collection and Analysis Methods, Report Writing and Presentation, Intellectual Property Rights (IPR), Patents, Copyrights, Trademarks |
| 22AIM73X | Professional Elective – 3 | Elective | 3 | Choice from: Robotics and Automation, Speech Recognition, Genetic Algorithms, Specific topics depend on chosen elective |
| 22AIM731 | Robotics and Automation (Professional Elective – 3) | Elective | 3 | Robot Kinematics and Dynamics, Sensors and Actuators in Robotics, Robot Control Architectures, Industrial Automation, Machine Vision for Robotics |
| 22AIM732 | Speech Recognition (Professional Elective – 3) | Elective | 3 | Speech Signal Processing, Phonetics and Phonology, Hidden Markov Models for Speech, Deep Learning for Speech Recognition, Speech Synthesis |
| 22AIM733 | Genetic Algorithms (Professional Elective – 3) | Elective | 3 | Optimization Problems, Genetic Algorithm Operators, Selection and Crossover, Mutation and Convergence, Applications of Genetic Algorithms |
| 22AIM74X | Professional Elective – 4 | Elective | 3 | Choice from: Ethical AI and Governance, Explainable AI (XAI), Quantum Machine Learning, Specific topics depend on chosen elective |
| 22AIM741 | Ethical AI and Governance (Professional Elective – 4) | Elective | 3 | AI Ethics Principles, Bias and Fairness in AI, AI Governance Frameworks, Regulatory Landscape for AI, Responsible AI Development |
| 22AIM742 | Explainable AI (XAI) (Professional Elective – 4) | Elective | 3 | Interpretability vs Explainability, Model-Agnostic Explainability Methods, LIME and SHAP, Feature Importance and Causal Inference, Evaluation of Explanations |
| 22AIM743 | Quantum Machine Learning (Professional Elective – 4) | Elective | 3 | Quantum Computing Basics, Quantum States and Qubits, Quantum Gates and Circuits, Quantum Algorithms for ML, Quantum Neural Networks |
| 22AIM75 | Project Work Phase - 1 | Project | 4 | Problem Identification and Scoping, Literature Survey and State-of-Art, Project Design and Methodology, Initial Implementation and Data Collection, Interim Report and Presentation |
| 22AIM76 | Technical Seminar | Skill | 3 | Topic Selection and Research, Literature Review and Synthesis, Presentation Skills, Technical Report Writing, Q&A and Discussion |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIM81 | Project Work Phase - 2 | Project | 8 | Advanced Implementation and Development, Testing, Debugging, and Optimization, Performance Evaluation, Comprehensive Documentation, Final Presentation and Demonstration |
| 22AIM82 | Internship / Industrial Training | Internship | 8 | Real-world Problem Solving, Industry Best Practices, Team Collaboration and Communication, Technical Report Generation, Professional Skill Development |
