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The complete, exam-focused guide to AI for GATE DA — search algorithms (informed, uninformed, adversarial), propositional & predicate logic, and reasoning under uncertainty with Bayesian networks, variable elimination, and sampling. Full syllabus, topic weightage, PYQ patterns, and the best course to score every mark.
Artificial Intelligence is a core scoring section in GATE DA, typically worth 6–10 marks. The syllabus has three parts: Search — uninformed (BFS, DFS), informed (A*, heuristics), and adversarial (minimax, alpha-beta pruning); Logic — propositional and predicate logic; and Reasoning under uncertainty — conditional independence representation (Bayesian networks), exact inference through variable elimination, and approximate inference through sampling. Highest-yield: A*, alpha-beta pruning, propositional logic entailment, and d-separation. These questions are algorithm-tracing based and highly predictable, making AI one of the most reliably scorable sections. Piyush Wairale's GATE DA AI course covers all of it with GATE-pattern practice and a test series.
The AI section of GATE DA is one of the most pattern-predictable parts of the paper. Search questions almost always test the same skills — tracing node expansion order in A*, checking heuristic admissibility, or counting pruned nodes in alpha-beta — and Bayesian network questions revolve around a small set of inference mechanics. Once you've mastered the algorithms by hand, these marks are essentially guaranteed.
The reasoning-under-uncertainty portion also compounds your Probability preparation: conditional independence, Bayes theorem, and joint distributions reappear here in graphical-model form. Preparing AI and Probability & Statistics together yields the best return for GATE DA 2027.
Every topic below is officially part of the GATE Data Science & AI syllabus for the AI section. Our course covers each one with theory, hand-traced examples, and GATE-pattern questions.
Based on GATE DA previous-year patterns, here is how the topics rank in exam importance. Prioritize the high-importance areas first.
| Topic | Exam Importance | Typical Question Style |
|---|---|---|
| A* & Informed Search | Very High | Node expansion order, admissible heuristic checks |
| Minimax & Alpha-Beta Pruning | Very High | Game-tree values, counting pruned nodes |
| Propositional Logic | High | Entailment, validity, satisfiability, CNF |
| Bayesian Networks & Conditional Independence | High | d-separation, joint probability from factorization |
| Uninformed Search (BFS/DFS/UCS/IDS) | High | Properties, time/space complexity, traversal order |
| Variable Elimination | Medium-High | Factor computation, elimination steps |
| Predicate Logic | Medium | Quantifier translation, unification |
| Sampling-Based Inference | Medium | Conceptual, rejection sampling behaviour |
Follow this sequence to build concepts logically and avoid gaps:
A dedicated, syllabus-mapped course covering search, logic, and reasoning under uncertainty for GATE Data Science & AI — with concept lectures, hand-traced algorithm examples (A*, alpha-beta, variable elimination), and topic-wise tests. Built by an IIT Madras M.Tech who has taught the IIT Madras BS Degree Programme.
Former instructor, IIT Madras BS Degree Programme • Microsoft Learn, AWS Academy & NPTEL Educator • 20,000+ students & 44,000+ YouTube subscribers.
The GATE DA AI syllabus covers three areas: (1) Search — informed, uninformed, and adversarial search; (2) Logic — propositional and predicate logic; (3) Reasoning under uncertainty — conditional independence representation, exact inference through variable elimination, and approximate inference through sampling.
AI is a core scoring section in GATE DA, typically carrying 6–10 marks. Search algorithm questions (A*, minimax, alpha-beta) and Bayesian inference questions are highly pattern-based and predictable, making AI one of the most reliably scorable sections once you master algorithm tracing.
The most frequently tested topics are A* search (admissibility, node expansion order), BFS/DFS properties, minimax with alpha-beta pruning (counting pruned nodes), propositional logic (entailment, satisfiability, CNF), Bayesian networks (conditional independence, d-separation), and variable elimination. Alpha-beta and A* questions appear almost every year.
The GATE DA Artificial Intelligence course by Piyush Wairale (M.Tech, IIT Madras) is designed specifically for the GATE Data Science & AI syllabus. It covers search, logic, and reasoning under uncertainty with GATE-pattern PYQs, algorithm-tracing practice, and a full test series at piyushwairale.com.
Start with uninformed search (BFS, DFS, uniform cost, iterative deepening) and their properties. Then master informed search — heuristics, admissibility, consistency, and A* tracing. Move to adversarial search: minimax and alpha-beta pruning with hand-traced game trees. Next cover propositional logic (truth tables, entailment, CNF, resolution) and predicate logic (quantifiers, unification). Finish with Bayesian networks: d-separation, variable elimination, and sampling. Practice GATE PYQs after each topic.
Yes. The reasoning-under-uncertainty portion covers conditional independence representation (the foundation of Bayesian networks), exact inference through variable elimination, and approximate inference through sampling. Questions typically test d-separation, computing joint probabilities from network factorization, and variable elimination mechanics.
Get the full syllabus, concept lectures, hand-traced algorithm practice, GATE-pattern questions, and test series — built for GATE Data Science & AI aspirants.