GATE Data Science Artificial Intelligence Syllabus

Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics – conditional independence representation, exact inference through variable elimination, and approximate inference through sampling.

Here’s an overview of GATE Data Science Artificial Intelligence Syllabus

Search Algorithms:

1. Informed Search: Informed search algorithms use problem-specific knowledge (heuristics) to guide the search towards the goal state more efficiently. Examples include A* search and greedy best-first search.
2. Uninformed Search: Uninformed search algorithms explore the search space without using any domain-specific knowledge. Examples include breadth-first search, depth-first search, and uniform-cost search.
3. Adversarial Search: Adversarial search, also known as game playing, involves searching through the possible moves and outcomes in a game, considering the actions of an opponent. Examples include minimax algorithm and alpha-beta pruning.

Logic:

1. Propositional Logic: Propositional logic deals with propositions (statements) that can be either true or false. It uses logical operators such as AND, OR, NOT, and implies to represent relationships between propositions.
2. Predicate Logic (First-order Logic): Predicate logic extends propositional logic by introducing variables, quantifiers (existential and universal), and predicates (relations between objects). It allows for more expressive representation of relationships and is widely used in AI and formal logic.

Must Read: Best GATE Data Science & AI Course 2025

Reasoning Under Uncertainty:

1. Conditional Independence Representation: Conditional independence represents the relationship between random variables such that the occurrence of one variable is independent of another variable given the value of a third variable.
2. Exact Inference through Variable Elimination: Variable elimination is a method for exact inference in probabilistic graphical models, such as Bayesian networks, by iteratively eliminating variables and updating probabilities based on evidence.
3. Approximate Inference through Sampling: Sampling-based methods, such as Markov Chain Monte Carlo (MCMC) and Gibbs sampling, are used for approximate inference in cases where exact inference is computationally intractable. These methods generate samples from the probability distribution to estimate the posterior distribution.

Understanding these concepts is crucial for designing intelligent systems that can search through large spaces efficiently, reason logically, and make decisions under uncertainty in real-world scenarios. They are fundamental to various applications in artificial intelligence, including planning, decision-making, and game playing.

GATE DA Subject wise syllabus:

1. GATE DA Linear Algebra Syllabus
2. GATE DA Calculus and Optimization Syllabus
3. GATE DA Probability and Statistics Syllabus
4. GATE DA Python Programming Data Structures and Algorithms Syllabus
5. GATE DA DBMS and Warehousing Syllabus
6. GATE DA Machine Learning
7. GATE DA  Artificial Intelligence Syllabus

Check out other post.