Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.
Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Explores NISQ devices and IBM Q, covering noisy quantum circuits, qubit technologies, and quantum algorithm development.
