GATE 2024 registration from August 24; know data science, AI syllabus, topics covered

NEW DELHI: The Indian Institute of Science Bangalore (IISc Bengaluru) will start the Graduate Aptitude Test in Engineering (GATE 2024) registration on August 24. The GATE 2024 exam will be held for a total of 30 subjects comprising full papers and sectional papers. Data Science and Artificial Intelligence paper has been introduced this year.

As per the GATE 2023 schedule announced earlier, the last date for submitting GATE application form is September 29. The deadline of the extended period to submit the form is October 13. The exam will be conducted on February 3, 4 10, 11 ,2024 for admission into the masters programme and recruitment by some public sector companies.

IISc Bangalore has announced a new two paper combination Computer Science and Information Technology, Data Science and Artificial Intelligence. As per the criteria, candidates will be allowed to appear in a maximum of two papers, however, for the second paper, they can only choose from a predefined two paper combination announced by the institute.

GATE 2024 Data Science and AI syllabus

The institute has uploaded the detailed GATE 2024 data science syllabus. Here are the topics covered under the data science and AI subject paper.

Probability and Statistics - Counting (permutation 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 more.

Linear Algebra - Vector space, subspaces, linear dependence and independence of vectors, matrices, projection matrix, orthogonal matrix, idempotent matrix etc.

Programming, Data Structures and Algorithms - Programming in Python, basic data structures: stacks, queues, linked lists, trees, hash tables; Search algorithms: linear search and binary search, basic sorting algorithms

Database Management and Warehousing - ER-model, relational model: relational algebra, tuple calculus, SQL, integrity constraints, normal form, file organization, indexing, data types, data transformation such as normalization, discretization, sampling, compression; data warehouse modelling.

Machine Learning - (i) Supervised 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, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross validation, multi-layer perceptron, feed-forward neural network;

(ii) Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple linkage, dimensionality reduction, principal component analysis.

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.