GATE New Test Paper on (DA) Data Science and Artificial Intelligence

                                                    Syllabus

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 covariance, random variables, discrete random variables and probability functions, uniform, Bernoulli, binomial distribution, Continuous random variables and probability distribution function, uniform, exponential, Poisson, normal, standard normal, t-distribution, chi-squared distributions, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test.

Linear Algebra: 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 composition, singular value decomposition

Calculus and Optimization: Functions of a single variable, limit, continuity differentiability, Taylor series, maxima and minima, optimization involving a single variable

Programming, Data Structure and Algorithms: Programming in Python, basic data structure: stacks, queues, linked lists, trees, hash tables, Search algorithms: linear search and binary search, 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.

Database Management and Warehousing: ER-model, relational model: relational algebra, tuple calculus, SOL, integrity constraints, normal form, file organization, indexing, data types, data transformation such as normalization, discretization, sampling, compression; data warehouse modelling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations

Machine Learning: (i) Supervised Learning: regression and classification 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-varia 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

Al: Search: informed, uninformed, adversarial, logic, propositional, predicate; reasoning under uncertainty topics conditional independence representation, exact inference through variable elimination, and approximate inference through sampling