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GATE Data Science and AI Latest Articles

GATE Data Science and AI Syllabus

Official GATE Data Science and AI Syllabus Get detailed explanation about GATE DA Syllabus Probability and Statistics: GATE Data Science and AI Syllabus Linear Algebra: GATE Data Science and AI Syllabus Calculus and Optimization: GATE Data Science and AI Syllabus ...

GATE Data Science and AI Syllabus | Artificial Intelligence (AI)

GATE Data Science Artificial Intelligence Syllabus Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics – conditional independence representation, exact inference through variable elimination, and approximate inference through sampling. Here’s an overview of GATE Data Science Artificial Intelligence ...

GATE Data Science and AI Syllabus | Machine Learning

GATE Data Science and AI Machine Learning Syllabus (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, ...

GATE Data Science and AI Syllabus | Database Management and Warehousing

GATE Data Science and AI DBMS and Warehousing Syllabus 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: schema for multidimensional ...

GATE Data Science and AI Syllabus | Programming, Data Structures and Algorithms

GATE Data Science and AI Programming Data Structures and Algorithms Syllabus Programming in Python, basic data structures: stacks, queues, linked lists, trees, hash tables; Search algorithms: linear search and binary search, basic sorting algorithms: selection sort, bubble sort and insertion ...

GATE Data Science and AI |Probability and Statistics Syllabus

<h2>GATE Data Science and AI Probability and Statistics Syllabus</h2> 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, ...