GATE Data Science & Artificial Intelligence (DA) Syllabus 2024: PDF Download, List of Chapters and Topics

GATE 2024 Preparation Strategy

GATE Sample Papers

Linear Algebra

• Matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix, and their properties
• Quadratic forms
• Determinant, rank, nullity, projections, LU decomposition, singular value decomposition
• Systems of linear equations and solutions
• Gaussian elimination
• Vector space, subspaces, linear dependence, and independence of vectors.
• Eigenvalues and Eigenvectors

Probability and Statistics

• Counting (permutation and combinations)
• Probability axioms
• Discrete random variables and probability mass functions
• Uniform, Bernoulli, binomial distribution
• Continuous random variables
• Correlation, covariance and random variables
• Probability distribution function
• T-distribution, chi-squared distributions
• Cumulative distribution function
• Conditional PDF
• 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
• Uniform, exponential, Poisson, normal, standard normal
• Central limit theorem, confidence interval, z-test, t-test, chi-squared test.

Calculus and OpƟmizaƟon

• Functions of a single variable
• Limit
• Maxima and minima
• Continuity and differentiability
• Taylor series
• Optimization involving a single variable

Database Management and Warehousing:

• calculus, SQL, integrity constraints, normal form, file organizaƟon, indexing, data types, data
• ER-model, relaƟonal model: relaƟonal algebra, tuple
• transformaƟon such as normalizaƟon, discreƟzaƟon, sampling, compression; data warehouse
• modelling: schema for mulƟdimensional data models, concept hierarchies, measures: categorizaƟon and computaƟons.

Programming, Data Structures and Algorithms

• Programming in Python
• Basic sorting algorithms: selection sort, bubble sort, and insertion sort
• Divide and conquer: mergesort, quicksort; introduction to graph theory
• Basic graph algorithms: traversals and shortest path.
• Basic data structures: stacks, queues, linked lists, trees, hash tables
• Search algorithms: linear search and binary search

AI

• Reasoning under uncertainty topics – conditional independence representation
• Exact inference through variable elimination
• Search: informed, uninformed
• Adversarial; logic, propositional, predicate
• Approximate inference through sampling

Machine Learning

• Supervised Learning: regression and classification problems
• K-nearest neighbor
• Naive Bayes classifier
• Simple linear regression, multiple linear regression, ridge regression, logistic regression
• Linear discriminant analysis
• Support vector machine
• K-folds cross-validation, multi-layer perceptron, feed-forward neural network
• Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering
• top-down, bottom-up: single-linkage, multiple- linkage, dimensionality reduction, principal component analysis
• Decision trees
• Bias-variance trade-off
• Cross-validation methods such as leave-one-out (LOO) cross-validation

GATE Civil Engineering (CE) Syllabus 2024

GATE 2024 Chemical Engineering Syllabus

Data Science & Artificial Intelligence

55

MCQ or MSQ or NAT

85

3 hours

General Aptitude

10

MCQ

15

Total

65

-

100

MCQs

1/3 for 1 mark questions 2/3 for 2 marks questions will be dedicated for each wrong answer

MSQ/NATs

No negative marking for MSQ and NATs questions

Artificial Intelligence: A Modern Approach

Stuart Russell and Peter Norvig

Introduction to Linear Algebra

Gilbert Strang

Introduction to Probability

Dimitri P. Bertsekas & John N. Tsitsiklis

Learning Python

Mark Lutz

Database Management Systems

Raghu Ramakrishnan and Johannes Gehrke

Machine Learning for Beginners

Chris Sebastian