Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical 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.
Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.
