Last week, we made the first steps towards building an agent that acts rationally: in words, it chooses an action that is an optimal trade off between what it prefers and what it believes it can achieve. At the moment, we are focussed on the belief bit of this trade off: how do we represent beliefs that reflect the degree to which the agent is certain, or uncertain, about whether a particular proposition is true? I introduced some notation in probability theory and its basic axioms. We're going to build on that this week, so that by the end of this week you have the means to compute an answer to probabilistic queries of the following form: given the evidence e that I've observed, what is the probability that proposition p is true?
We will be covering the following topics:
22: Probabilities and Bayes' Rule
23: Probabilistic Reasoning with Bayesian Networks
24: Exact Inference in Bayesian Networks
As always, these lectures will be delivered in person and live streamed. But in addition, each of the above includes pre-recorded videos of the lectures (with edited captions), the slides that were used in the videos, required readings, and a post-lecture quiz. The quiz is a chance for you to gauge your understanding of the material presented here, and so we strongly encourage you to review this content in the above order, and then complete the quiz. If there is anything you don't understand, then you have several options:
- Post a question on piazza;
- Ask a question at the in-persion lecture; and/or
- Ask your tutor.
For coursework, you can also get your queries addressed by attending the demonstrators' teaching hour.