Random Number Generators: Basics and Algorithms

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Covers the implementation of deterministic random number generation in C++ simulations.

Covers basic statistics concepts, confidence intervals, t distribution, and experimental study strategies.

Explores functional random generators for various domains and their application in systematic random testing.

Introduces Jupyter exercises on Differential Privacy, covering random generators, understanding data intrusion impact, and practical applications.

Covers the concept of tokens, how they work, and the implementation of two-factor authentication.

Covers classical testing of qubits, including measuring in different bases and obtaining uniform results.

Explores standard C libraries, error handling, random number generation, and custom printf variants.

Explores C++ standard library containers and algorithms, including associative tables, stacks, queues, and random number generation.

Explores containers and algorithms in the C++ standard library, including find, copy, and sort functionalities.

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.