| Probability and Statistics |
Permutation and Combinations, Probability Axioms, Sample Space, Events, Independent Events, Mutually Exclusive Events, Marginal, Conditional and Joint Probability and Bayes Theorem |
| Linear Algebra |
Vector Space, Subspaces, Linear Dependence and Independence of Vectors, Matrices, Orthogonal Matrix, Idempotent Matrix, Quadratic Forms, Systems of Linear Equations and Solutions |
| Programming, Data Structures and Algorithms |
Programming in Python, Basic Data Structures: Stacks, Queues, Linked Lists, Trees, Hash Tables and Search Algorithms |
| Database Management and Warehousing |
ER-model, Relational Model, Tuple Calculus, SQL, Integrity Constraints, Normal Form, File Organisation, Indexing, Data Types, Data Transformation such as Normalisation, Sampling, Compression; Data Warehouse Modelling: Schema for Dimensional Data Models, Concept Hierarchies, Measures: Categorisation and Computations |
| Machine Learning |
Supervised Learning and Unsupervised Learning |
| 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 |
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