This is a reminder for the bonus lecture (a probability refresher) I will be giving tomorrow (Friday, Jan 7) at 1pm in Annenberg 213.
Here is the plan - we'll start by recalling the definition of a random variable and then review what discrete and continuous random variables are. Following that, we'll revisit the different types of distribution functions (probability mass function, probability density function, cumulative distribution function) and discuss some standard and well known discrete and continuous random variables (Bernoulli, binomial, geometric, Poisson, uniform, exponential, normal). Then we'll define expectation of a random variable and review some of the important properties of the expectation, with particular emphasis on linearity and its applications using indicator random variables (useful for HW1).
You don't have to attend the lecture if you are really comfortable with these concepts (and can comfortably solve Problem 2 of HW1). But if you've never really kept in touch with probability for a long time, then you'd probably benefit from the lecture.
It'll be a short lecture (about one hour). The slides are pretty verbose, and could serve as a reference later on. They will be posted on the course website after the lecture tomorrow.
Update: They are now posted on the course website.
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