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 fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
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.
