GATE Data Science and Artificial Intelligence Syllabus 2026, Download PDF

• 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, and Standard Deviation
• Correlation and Covariance
• Random Variables
• Discrete Random Variables and Probability Mass Functions (Uniform, Bernoulli, and Binomial Distribution)
• Continuous Random Variables and Probability Distribution Functions (Uniform, Exponential, Poisson, Normal, Standard Normal, t-Distribution, Chi-Squared Distributions)
• Cumulative Distribution Function
• Conditional Probability Density Function
• Central Limit Theorem
• Confidence Interval
• z-Test
• t-Test
• Chi-Squared Test

• Vector Space
• Subspaces
• Linear Dependence and Independence of Vectors
• Matrices
• Projection Matrix
• Orthogonal Matrix
• Idempotent Matrix
• Partition Matrix and Their Properties
• Quadratic Forms
• Systems of Linear Equations and Solutions
• Gaussian Elimination
• Eigenvalues and Eigenvectors
• Determinant
• Rank
• Nullity
• Projections
• LU Decomposition
• Singular Value Decomposition

• Functions of a Single Variable
• Limit
• Continuity and Differentiability
• Taylor Series
• Maxima and Minima
• Optimization Involving a Single Variable

• Programming in Python
• Basic Data Structures: Stacks, Queues, Linked Lists, Trees, and Hash Tables
• Search Algorithms: Linear Search and Binary Search
• Basic Sorting Algorithms: Selection Sort, Bubble Sort, Insertion Sort
• Divide and Conquer Techniques: Mergesort, Quicksort
• Introduction to Graph Theory
• Basic Graph Algorithms: Traversals and the Shortest Path

• ER-Model (Entity-Relationship Model)
• Relational Model: Relational Algebra, Tuple Calculus
• SQL (Structured Query Language)
• Integrity Constraints
• Normal Form
• File Organization
• Indexing
• Data Types
• Data Transformation: Normalization, Discretization, Sampling, and Compression
• Data Warehouse Modeling: Schema for Multidimensional Data Models
• Concept Hierarchies
• Measures: Categorization and Computations

• Supervised Learning
• Regression and Classification Problems
• Simple Linear Regression
• Multiple Linear Regression
• Ridge Regression
• Logistic Regression
• k-Nearest Neighbors
• Naive Bayes Classifier
• Linear Discriminant Analysis
• Support Vector Machine
• Decision Trees
• Bias-Variance Trade-off
• Cross-validation Methods: Leave-One-Out (LOO) Cross-validation, k-Folds Cross-validation
• Multi-layer Perceptron
• Feed-forward Neural Network
• Unsupervised Learning:
• Clustering Algorithms
• k-Means and k-Medoid Clustering
• Hierarchical Clustering
• Dimensionality Reduction
• Principal Component Analysis (PCA)

• Search: Informed Search, Uninformed Search, Adversarial Search
• Logic: Propositional Logic, Predicate Logic
• Reasoning under Uncertainty Topics
• Conditional Independence Representation
• Exact Inference through Variable Elimination
• Approximate Inference through Sampling

GATE Data Science and Artificial Intelligence Exam Pattern 2026

• General Aptitude: 15 marks
• Subject Questions: 85 marks
• Total Marks: 100 marks

• Applicable only to the wrong answer selected in an MCQ
• For 1 Mark Wrong Answer, Deduction of 1/3 Marks Applicable
• For 2 Marks Wrong Answer, Deduction of 2/3 Marks Applicable