Explores regularization, learning algorithms, and subgaussian assumptions in machine learning.
Explores Markov chain games, hitting probabilities, and expected hitting times in a target set.
Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.
Explores NISQ devices and IBM Q, covering noisy quantum circuits, qubit technologies, and quantum algorithm development.
Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.
