GATE 2024 Syllabus For Data science and AI Out at gate2024.iisc.ac.in: Direct Link Here

IISc Bangalore has released the GATE 2024 Syllabus for Data science and AI. Candidates can download the syllabus at gate2024.iisc.ac.in.

Mohith Kumar

Updated on - Mar 4, 2024 | 06:10 PM IST

GATE 2024 Syllabus For Data science and AI Out at gate2024.iisc.ac.in: Direct Link Here

GATE 2024 Syllabus For Data science and AI Released (Image Credits: pexels.com).

GATE 2024 Syllabus: The Indian Institute of Science (IISc) Bangalore, has released the Data science and Artificial Intelligence (AI) syllabus for the Graduate Aptitude Test in Engineering (GATE 2024). Candidates who are attending the GATE 2024 examination can download the GATE Data Science and AI Syllabus 2024 at the official website- gate2024.iisc.ac.in.

The GATE 2024 examination will be held on Feb 3, 4, 10 and 11, 2024. Candidates can download the GATE 2024 syllabus at the official website. The direct link to download the Data science and Artificial Intelligence (AI) syllabus is given in the table below.

Particular	Link
GATE Data Science and AI Syllabus 2024 Download Link	Click Here

Also Read: CLAT Registration 2024 Ends on Nov 10

Detailed Data science and AI GATE 2024 Syllabus

Check the detailed Data science and AI GATE 2024 Syllabus from the table below.

Chapters	Topics
Probability and Statistics	Permutation and Combinations, Probability Axioms, Sample Space, Events, Independent Events, Mutually Exclusive Events, Marginal, Conditional and Joint Probability and Bayes Theorem
Linear Algebra	Vector Space, Subspaces, Linear Dependence and Independence of Vectors, Matrices, Orthogonal Matrix, Idempotent Matrix, Quadratic Forms, Systems of Linear Equations and Solutions
Programming, Data Structures and Algorithms	Programming in Python, Basic Data Structures: Stacks, Queues, Linked Lists, Trees, Hash Tables and Search Algorithms
Database Management and Warehousing	ER-model, Relational Model, Tuple Calculus, SQL, Integrity Constraints, Normal Form, File Organisation, Indexing, Data Types, Data Transformation such as Normalisation, Sampling, Compression; Data Warehouse Modelling: Schema for Dimensional Data Models, Concept Hierarchies, Measures: Categorisation and Computations
Machine Learning	Supervised Learning and Unsupervised Learning
Artificial intelligence	Search: Informed, Uninformed, Adversarial; Logic, Propositional, Predicate; Reasoning Under Uncertainty Topics - Conditional Independence Representation, Exact Inference Through Variable Elimination, and Approximate Inference Through Sampling