VTU 2022 Scheme  ·  Degree  ·  AIML

ARTIFICIAL INTELLIGENCE BAI402 BAD402

Module-wise notes, PYQs, and a built-in resource explorer — everything you need to crack BAD402 in one focused page.

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CodeBAD402
Credits04
CIE / SEE50 / 50
TypeTheory
Exam3 Hours
Hours / Week3:0:2:0
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Last Updated:  15 March 2026

Module Overview

M1

Module 1 Overview

Introduction: What is AI? Foundations and History of AI Intelligent Agents: Agents and environment, Concept of Rationality, The nature of environment, The structure of agents.

Text book 1: Chapter 1- 1.1, 1.2, 1.3 Chapter 2- 2.1, 2.2, 2.3, 2.4

M2

Module 2 Overview

Problem"solving: Problem"solving agents, Example problems, Searching for Solutions Uninformed Search Strategies: Breadth First search, Depth First Search, Iterative deepening depth first search;

Text book 1: Chapter 3- 3.1, 3.2, 3.3, 3.4

M3

Module 3 Overview

Informed Search Strategies: Heuristic functions, Greedy best first search, A*search. Heuristic Functions

Logical Agents: Knowledge - based agents, The Wumpus world, Logic, Propositional logic, Reasoning patterns in Propositional Logic

Text book 1: Chapter 3-3.5,3.6 Chapter 4 - 4.1, 4.2 Chapter 7- 7.1, 7.2, 7.3, 7.4, 7.5

M4

Module 4 Overview

First Order Logic: Representation Revisited, Syntax and Semantics of First Order logic, Using First Order logic. Inference in First Order Logic :Propositional Versus First Order Inference, Unification, Forward Chaining, Backward Chaining, Resolution

Text book 1: Chapter 8- 8.1, 8.2, 8.3 Chapter 9- 9.1, 9.2, 9.3, 9.4, 9.5

M5

Module 5 Overview

Uncertain Knowledge and Reasoning: Quantifying Uncertainty: Acting under Uncertainty, Basic Probability Notation, Inference using Full Joint Distributions, Independence, Baye's Rule and its use. Wumpus World Revisited

Expert Systems: Representing and using domain knowledge, ES shells. Explanation, knowledge acquisition

Text Book 1: Chapter 13-13.1, 13.2, 13.3, 13.4, 13.5, 13.6 Text Book 2: Chapter 20

ARTIFICIAL INTELLIGENCE BAI402 is a VTU course covered through module-wise syllabus, notes, and PYQ-driven exam practice available on this page.

Credits for BAD402: 04.

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

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

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

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Resource Explorer

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Frequently Asked Questions

What is BAD402 (ARTIFICIAL INTELLIGENCE BAI402)?

ARTIFICIAL INTELLIGENCE BAI402 is a VTU course covered through module-wise syllabus, notes, and PYQ-driven exam practice available on this page.

How many credits is BAD402?

Credits for BAD402: 04.

Are notes and previous year question papers available for BAD402?

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

How should I prepare ARTIFICIAL INTELLIGENCE BAI402 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 BAD402 page updated for current VTU scheme?

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

Explore More VTU Notes

About ARTIFICIAL INTELLIGENCE (BAI402)

ARTIFICIAL INTELLIGENCE (BAI402) 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.

Success Strategy

Highlight definitions, advantages/disadvantages, and use case examples. Clear headings and bullet points are essential for VTU evaluators.

📘 Detailed Syllabus & Topic Breakdown

Detailed Subject Overview

ARTIFICIAL INTELLIGENCE (BAI402) 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
Essential

Master the Introduction What is AI? Foundations and History of AI Intelligent Agents: Agents and environment, Concept of Rationality, The nature of environment, The structure...

Key: Exam Priority Concept
Module 2
Math Heavy

Master the Problem"solving Problem"solving agents, Example problems, Searching for Solutions Uninformed Search Strategies: Breadth First search, Depth First Search, Iterative d...

Key: Exam Priority Concept
Module 3
Logic Core

Master the Informed Search Strategies Heuristic functions, Greedy best first search, A*search. Heuristic Functions...

Key: Exam Priority Concept
Module 4
Exam Focus

Master the First Order Logic Representation Revisited, Syntax and Semantics of First Order logic, Using First Order logic. Inference in First Order Logic :Propositional Versus Fir...

Key: Exam Priority Concept
Module 5
High Weight

Master the Uncertain Knowledge and Reasoning Quantifying Uncertainty: Acting under Uncertainty, Basic Probability Notation, Inference using Full Joint Distributions, Independence, Baye's Rule and...

Key: Exam Priority Concept

Professional Career Relevance

AI foundations pave the way for Machine Learning Engineering, Natural Language Processing, and Research roles in OpenAI and DeepMind. 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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