VTU 2025 Scheme  ·  Degree  ·  First Year

Introduction to AI and its Applications 1BAIA103

Module-wise notes, PYQs, and a built-in resource explorer — everything you need to crack 1BAIA103 in one focused page.

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Code1BAIA103
Credits03
CIE / SEE50 / 50
TypeTheory
Exam3 Hours
Hours / Week2:2:2:0
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Last Updated:  15 March 2026

Syllabus Overview

M1

Module 1: Introduction to Artificial Intelligence

Artificial Intelligence, How Does AI Work?, Advantages and Disadvantages of Artificial Intelligence, History of Artificial Intelligence, Types of Artificial Intelligence, Weak AI, Strong AI, Reactive Machines, Limited Memory, Theory of Mind, Self -Awareness, Is Artificial Intelligence Same as Augmented Intelligence and Cognitive Computing, Machine Learning and Deep Learning. Machine Intelligence: Defining Intelligence, Components of Intelligence, Differences Between Human and Machine Intelligence, Agent and Environment, Search, Uninformed Search Algorithms, Informed Search Algorithms: Pure Heuristic Search, Best-First Search Algorithm (Greedy Search). Knowledge Representation: Introduction, Knowledge Representation, Knowledge -Based Agent, Types of Knowledge. Textbook 1: Chapter 1 (1.1-1.5), Chapter 3 (3.1-3.7.2), Chapter 4 (4.1-4.4)

M2

Module 2: Detailed Syllabus

Introduction to Prompt Engineering , Introduction to Prompt Engineering, The Evolution of Prompt Engineering, Types of Prompts, How Does Prompt Engineering Work?, Comprehending Prompt Engineering's Function in Communication, The Advantages of Prompt Engineering, The Future of LLM Communication. Prompt Engineering Techniques for ChatGPT , Introduction to Prompt Engineering Techniques, Instructions Prompt Technique, Zero, One, and Few Shot Prompting, Self-Consistency Prompt. Prompts for Creative Thinking: Introduction, Unlocking Imagination and Innovation. Prompts for Effective Writing: Introduction, Igniting the Writing Process with Prompts. Textbook 2: Chapters 1, 3, 4 & 5

M3

Module 3: Machine Learning

Techniques in AI, Machine Learning Model, Regression Analysis in Machine Learning, Classification Techniques, Clustering Techniques, Naïve Bayes Classification, Neural Network, Support Vector Machine (SVM). Textbook 1: Chapter 2 (2.1-2.8)

M4

Module 4: Trends in AI

AI and Ethical Concerns, AI as a Service (AIaaS), Recent trends in AI, Expert System, Internet of Things, Artificial Intelligence of Things (AIoT). Textbook 1: Chapter 8 (8.1, 8.2, 8.4), Chapter 9 (9.1- 9.3)

M5

Module 5: Detailed Syllabus

Robotics, Robotics-an Application of AI, Drones Using AI, No Code AI, Low Code AI. Textbook 1: Chapter 8 (8.3), Chapter 1 (1.7, 1.8, 1.10, 1.11) Industrial Applications of AI: Application of AI in Healthcare, Application of AI in Finance, Application of AI in Retail, Application of AI in Agriculture, Application of AI in Education, Application of AI in Transportation, AI in Experimentation and Multi-disciplinary research. Textbook 3: Chapter 3, Chapter 5 (5.1)

Textbooks & Resources

  • Reema Thareja, Artificial Intelligence: Beyond Classical AI, Pearson Education, 2023.
  • Ajantha Devi Vairamani and Anand Nayyar, Prompt Engineering: Empowering Communication , 1st Edition, CRC Press, Taylor & Francis Group, 2024. (DOI: https://doi.org/10.1201/92319).
  • Saptarsi Goswami, Amit Kumar Das and Amlan Chakrabarti, “AI for Everyone – A Beginner’s Handbook for Artificial Intelligence”, Pearson, 2024.

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Frequently Asked Questions

What is 1BAIA103 (Introduction to AI and its Applications)?

Introduction to AI and its Applications (1BAIA103) is a VTU course covered through module-wise syllabus, notes, and PYQ-driven exam practice available on this page.

How many credits is 1BAIA103?

Credits for 1BAIA103: 04.

Are notes and previous year question papers available for 1BAIA103?

Yes. You can access organized notes, PDFs, and PYQ material from the file explorer/resources section on this page.

How should I prepare Mathematics-I 1BAIA103 for VTU exams?

Start with module summaries, solve recent PYQs unit-wise, and finish with complete paper practice under time constraints for SEE readiness.

Is this 1BAIA103 page updated for current VTU scheme?

Yes, this page is maintained with current scheme-oriented materials and practical exam-focused resource curation.

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About Mathematics-I (1BAIA103)

Mathematics-I (1BAIA103) is a critical course in the VTU curriculum, essential for any student looking to master the foundations of engineering. It covers key theoretical frameworks and practical concepts that are widely used in the industry today, ensuring students are well-prepared for both exams and their future careers.

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📘 Detailed Syllabus & Topic Breakdown

Detailed Subject Overview

Mathematics-I (1BAIA103) is designed to provide a comprehensive look into the core methodologies and advanced theories that define this field. Understanding this subject is fundamental for anyone looking to excel in modern technical domains and industrial engineering.

By studying this course, you will learn how to approach complex problems with a structured mindset, optimizing systems for better performance and reliability—skills that are highly valued in both AI research and software architecture.

Module-by-Module Breakdown

Module 1
Core

Introduction to Artificial Intelligence: Artificial Intelligence, How Does AI Work?, Advantages and Disadvantages of Artificial Intelligence, History of Artificial Intelligence, Types of Artificial Intelligence, Weak AI, ...

Module 2
Core

Detailed Syllabus: Introduction to Prompt Engineering , Introduction to Prompt Engineering, The Evolution of Prompt Engineering, Types of Prompts, How Does Prompt Engineering Work?, Comprehending Pro...

Module 3
Core

Machine Learning: Techniques in AI, Machine Learning Model, Regression Analysis in Machine Learning, Classification Techniques, Clustering Techniques, Naïve Bayes Classification, Neural Network, Sup...

Module 4
Core

Trends in AI: AI and Ethical Concerns, AI as a Service (AIaaS), Recent trends in AI, Expert System, Internet of Things, Artificial Intelligence of Things (AIoT). Textbook 1: Chapter 8 (8.1, 8.2,...

Module 5
Core

Detailed Syllabus: Robotics, Robotics-an Application of AI, Drones Using AI, No Code AI, Low Code AI. Textbook 1: Chapter 8 (8.3), Chapter 1 (1.7, 1.8, 1.10, 1.11) Industrial Applications of AI: Appl...

Professional Career Relevance

This subject provides a strong foundation for various technical roles, emphasizing analytical thinking, system design, and the practical application of engineering principles in the modern industry. Mastering these concepts prepares you for high-demand roles in Data Science, System Architecture, and Technical Leadership in top-tier tech companies.

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