Your interpretation of the author's use of $\sim$ seems about right. Based on how the slide is typeset, it seems like saving space was more important than precision.
A binomial distribution is characterized by three attributes:
- a fixed number of binary trials is conducted such that
- each trial is independent
- with a fixed probability of success.
So it's appropriate to use a binomial model in these circumstances. Suppose an experiment consists of flipping a coin (assume each flip is independent) with fixed probability of heads a fixed number of times and recording the number of heads. The number of heads you observe has a binomial distribution.
The appearance of a binomial likelihood in Bayes' rule, as opposed to appearing in a different context (such as MLE), does not change how the binomial distribution is characterized. So if you're familiar with a binomial likelihood elsewhere, there's not a difference when it appears in a Bayesian context.