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.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.
