The Lec10 slides are not on the 07-380 course site yet, so this guide uses only the PR6 Bayes Nets pre-reading and the 15-281 Bayes Net Demo. A joint distribution can answer any query, but nobody hands it to you and it is too big to store; a Bayes net writes it as a product of 'node given parents' tables, and every missing edge is an independence assumption.
Week 2 is about probability: conditional probability and Bayes' rule turn evidence into probabilities; a Bayesian network uses arrows to split a joint probability into one conditional probability table per node, inference by enumeration is exact but slow, and rejection sampling and likelihood weighting approximate it with samples; a Markov chain describes a state that changes over time with a transition model, and a hidden Markov model adds a sensor model to work back from observations to states you cannot see. With time links for every section, plus the approach and checks for the Heredity and PageRank projects.
Lectures 13–18 and Project 4 move from factor operations and variable elimination to exact inference and particle filtering, letting Pacman track invisible ghosts through noisy distance sensors.
CS228 is Stanford's probabilistic graphical models course, in three parts — representation, inference and learning: graphs write down independence assumptions so that exponentially large probability computations become polynomial. What self-learners can get is the 16-chapter public notes, cs228-notes; lecture slides, recordings and the current assignments are not available.