Focuses on machine learning, data analytics, artificial intelligence, and statistical modeling, preparing candidates for cutting-edge roles in data science and AI.

GATE Data Science and Artificial Intelligence

Official 2025 syllabus extracted from https://gate2025.iitr.ac.in/doc/2025/GATE%20_DA_2025_Syllabus.pdf

Probability and Statistics

Counting (permutations and combinations)

Probability axioms

Sample space, events

Independent events

Mutually exclusive events

Marginal, conditional and joint probability

Bayes Theorem

Conditional expectation and variance

Mean, median, mode, standard deviation

Correlation and covariance

Discrete random variables

Probability mass functions: uniform, Bernoulli, binomial

Continuous random variables

Probability distribution functions: uniform, exponential, Poisson, normal, standard normal, t-distribution, chi-squared

Cumulative distribution function

Conditional PDF

Central limit theorem

Confidence interval

z-test, t-test, chi-squared test

Linear Algebra

Vector space and subspaces

Linear dependence and independence

Matrices

Projection matrix

Orthogonal matrix

Idempotent matrix

Partition matrix

Quadratic forms

Systems of linear equations and solutions

Gaussian elimination

Eigenvalues and eigenvectors

Determinant, rank, nullity

Projections

LU decomposition

Singular value decomposition

Calculus and Optimization

Functions of a single variable

Limit, continuity and differentiability

Taylor series

Maxima and minima

Optimization involving a single variable

Programming, Data Structures and Algorithms

Programming in Python

Stacks, queues

Linked lists

Trees

Hash tables

Linear search and binary search

Selection sort, bubble sort, insertion sort

Mergesort, quicksort (divide and conquer)

Graph theory introduction

Graph traversals

Shortest path algorithms

Database Management and Warehousing

ER-model

Relational algebra

Tuple calculus

Integrity constraints

Normal forms

File organization

Indexing

Data types

Normalization

Discretization, sampling, compression

Multidimensional data models

Concept hierarchies

Measures: categorization and computation

Machine Learning

Regression and classification problems

Simple linear regression

Multiple linear regression

Ridge regression

Logistic regression

k-nearest neighbour

Naive Bayes classifier

Linear discriminant analysis

Support vector machine

Decision trees

Bias-variance trade-off

Leave-one-out cross-validation

k-folds cross-validation

Multi-layer perceptron

Feed-forward neural network

Supervised Learning

k-means / k-medoid clustering

Hierarchical clustering: top-down, bottom-up

Single-linkage, multiple-linkage

Dimensionality reduction

Principal component analysis

Unsupervised Learning

Informed search

Uninformed search

Adversarial search

Propositional logic

Predicate logic

Reasoning under uncertainty

Conditional independence representation

Exact inference through variable elimination

Approximate inference through sampling