I'm teaching a course this fall on Discrete Structures for Computer Science. It's taught out of the math department but is a service course for the CS department with 90-95% of students being CS majors. I've taught the course before a few times, and this fall we are introducing a content unit on discrete probability that hasn't been there before. The other content units cover sets, counting, logic, and proof. Discrete probability will come last, after all the other stuff is covered.
The problem is that the book I'm using doesn't have any discrete probability in it. Which isn't really a problem as long as I can find a good, free online supplement for students to use. The learning outcomes for this unit are:
- Calculate probabilities of events and expectations of random variables for elementary problems such as games of chance.
- Differentiate between dependent and independent events.
- Identify a case of the binomial distribution and compute a probability using that distribution.
- Apply Bayes' theorem to determine conditional probabilities in a problem.
- Compute the variance for a given probability distribution.
- Explain how events that are independent can be conditionally dependent and identify real world examples of such cases.
Those learning outcomes are taken from the ACM recommendations for discrete probability, in discrete math courses for CS majors.
A brief Google search brings up a ton of free books and video resources that are way too high of a level for the course -- as in, graduate-level stuff whereas this course is mainly second-year CS majors with minimal math backgrounds. The closest match I found was this wikibook but it doesn't seem to cover all the bases. So I'm wondering if you've got anything to share. Thanks in advance.