GATE 2025:The curriculum covers a wide range of topics including machine learning, data mining, natural language processing, computer vision, and more, providing students with a comprehensive understanding of the principles and techniques used in data science and artificial intelligence.
Probability and Statistics
- Counting (Permutations and Combinations)
- Probability Axioms
- Sample Space and Events
- Independent Events
- Mutually Exclusive Events
- Marginal, Conditional, and Joint Probability
- Bayes Theorem
- Conditional Expectation and Variance
- Measures of Central Tendency and Dispersion
- Mean, Median, Mode, and Standard Deviation
- Correlation and Covariance
- Random Variables
- Discrete Random Variables and Probability Mass Functions
- Uniform, Bernoulli, Binomial Distributions
- Continuous Random Variables and Probability Distribution Functions
- Uniform, Exponential, Poisson, Normal Distributions
- Standard Normal Distribution, t-Distribution, Chi-Squared Distributions
- Cumulative Distribution Function
- Conditional Probability Density Function
- Central Limit Theorem
- Confidence Intervals and Hypothesis Testing
- z-Test, t-Test, Chi-Squared Test
Linear Algebra
- Vector Space and Subspaces
- Linear Dependence and Independence of Vectors
- Matrices and Operations
- Projection Matrix and Orthogonal Matrix
- Idempotent Matrix and Properties
- Quadratic Forms
- Systems of Linear Equations and Solutions
- Gaussian Elimination
- Eigenvalues and Eigenvectors
- Determinant, Rank, and Nullity
- LU Decomposition
- Singular Value Decomposition
Calculus and Optimization
- Functions of a Single Variable
- Limit, Continuity, and Differentiability
- Taylor Series
- Maxima and Minima
- Optimization
Programming, Data Structures, and Algorithms
- Programming in Python
- Basic Data Structures
- Stacks, Queues, Linked Lists, Trees, Hash Tables
- Search Algorithms
- Linear Search, Binary Search
- Basic Sorting Algorithms
- Selection Sort, Bubble Sort, Insertion Sort
- Divide and Conquer
- Merge Sort, Quick Sort
- Introduction to Graph Theory
- Basic Graph Algorithms: Traversals and Shortest Path
Database Management and Warehousing
- ER-Model
- Relational Model
- Relational Algebra, Tuple Calculus
- SQL
- Integrity Constraints
- Normal Form
- File Organization and Indexing
- Data Types and Transformations
- Normalization, Discretization, Sampling, Compression
- Data Warehouse Modeling
- Schema for Multidimensional Data Models
- Concept Hierarchies and Measures
Machine Learning
- Supervised Learning
- Regression and Classification Problems
- Regression Techniques
- Classification Algorithms
- Support Vector Machine, Decision Trees, Neural Networks
- Bias-Variance Trade-off, Cross-Validation
- Unsupervised Learning
- Clustering Algorithms
- Dimensionality Reduction
- Principal Component Analysis
Artificial Intelligence
- Search Algorithms
- Informed, Uninformed, Adversarial
- Logic
- Propositional, Predicate
- Reasoning under Uncertainty
- Conditional Independence Representation
- Exact and Approximate Inference
The syllabus for the GATE New Test Paper on Data Science and Artificial Intelligence (DA) is comprehensive and covers a wide array of topics ranging from Probability and Statistics to Artificial Intelligence. It is designed to test candidates on their understanding and proficiency in various areas crucial for the field of Data Science and Artificial Intelligence.
Conclusion
In conclusion, the GATE New Test Paper on Data Science and Artificial Intelligence (DA) encompasses a diverse range of topics essential for anyone aspiring to excel in the field. By thoroughly studying and understanding the syllabus outlined, candidates can enhance their knowledge and skills, thereby increasing their chances of success in the examination and beyond.
