Artificial Intelligence for GATE 2025 Last Updated : 23 Jul, 2025 Comments Improve Suggest changes 5 Likes Like Report Welcome to the complete tutorial on Artificial Intelligence for the GATE DA Exam. This guide will simplify the syllabus topics, making them accessible and straightforward to understand for all aspirants.1. Introduction to AI and Search AlgorithmsUninformed SearchBreadth-First SearchDepth-First SearchUniform-Cost SearchInformed SearchGreedy SearchA* SearchHeuristic FunctionsAdversarial Search Minimax AlgorithmAlpha-Beta Pruning2. Logic in AIPropositional LogicPredicate Logic and Quantifiers3. Reasoning Under Uncertainty in AI Conditional IndependenceRepresentation of Uncertainty using Bayesian NetworksExact Inference: Variable EliminationApproximate Inference through SamplingOfficial Syllabus of Artificial Intelligence for GATE DA Here's the complete syllabus for Artificial Intelligence as per the GATE DA 2025 official notification:AI Search: informed, uninformed, adversarialLogic: propositional, predicateReasoning under uncertainty topics: conditional independence representation, exact inference through variable elimination, and approximate inference through samplingGATE DA (Data Science and AI) Subject Wise Weightage 2025The subject-wise weightage for the GATE DA exam, based on analysis of previous years' exams, is as follows:SubjectNumber of QuestionsTotal MarksGeneral Aptitude1015Probability and Statistics1016Linear Algebra610Calculus and Optimization58Programming, Data Structures and Algorithms1321Database Management and Warehousing68Machine Learning811Artificial Intelligence711Total65100Tips For Candidates While Preparing for Artificial Intelligence in GATE ExamsMaster the Basics: Before tackling advanced AI topics, ensure a solid understanding of core principles like basic search algorithms and logic.Visual Learning: Many AI concepts, such as neural networks and Bayesian networks, are best understood visually. Try to sketch diagrams to visualize these concepts better.Practice Problem Solving: Applying AI techniques in practical scenarios can significantly enhance your understanding. Work on problems that involve implementing different AI algorithms.Simulate Exam Conditions: Frequently practice under timed conditions to better manage the pressure of the actual exam.Regular Revision: AI concepts can be intricate. Regular review is crucial to retain knowledge over extended periods.This tutorial offers a comprehensive yet clear approach to mastering Artificial Intelligence for the GATE DA 2025 exam. By systematically breaking down each topic and explaining it in simple terms, you're set to excel in both your understanding and exam performance. Create Quiz Comment S sanjulika_sharma Follow 5 Improve S sanjulika_sharma Follow 5 Improve Article Tags : Artificial Intelligence GATE 2025 GATE DA Explore Introduction to AIWhat is Artificial Intelligence (AI)10 min readTypes of Artificial Intelligence (AI)6 min readTypes of AI Based on Functionalities4 min readAgents in AI7 min readArtificial intelligence vs Machine Learning vs Deep Learning3 min readProblem Solving in Artificial Intelligence6 min readTop 20 Applications of Artificial Intelligence (AI) in 202513 min readAI ConceptsSearch Algorithms in AI6 min readLocal Search Algorithm in Artificial Intelligence7 min readAdversarial Search Algorithms in Artificial Intelligence (AI)15+ min readConstraint Satisfaction Problems (CSP) in Artificial Intelligence10 min readKnowledge Representation in AI9 min readFirst-Order Logic in Artificial Intelligence4 min readReasoning Mechanisms in AI9 min readMachine Learning in AIMachine Learning Tutorial6 min readDeep Learning Tutorial5 min readNatural Language Processing (NLP) Tutorial5 min readComputer Vision Tutorial7 min readRobotics and AIArtificial Intelligence in Robotics5 min readWhat is Robotics Process Automation8 min readAutomated Planning in AI8 min readAI in Transportation8 min readAI in Manufacturing : Revolutionizing the Industry6 min readGenerative AIWhat is Generative AI?7 min readGenerative Adversarial Network (GAN)11 min readCycle Generative Adversarial Network (CycleGAN)7 min readStyleGAN - Style Generative Adversarial Networks5 min readIntroduction to Generative Pre-trained Transformer (GPT)4 min readBERT Model - NLP12 min readGenerative AI Applications 7 min readAI PracticeTop Artificial Intelligence(AI) Interview Questions and Answers15+ min readTop Generative AI and LLM Interview Question with Answer15+ min read30+ Best Artificial Intelligence Project Ideas with Source Code [2025 Updated]15+ min read Like