Schedule (topic flow)

A 15-week pacing plan — Blackboard is authoritative for dates and deadlines

Important

This is a pacing plan, not a calendar. It shows the order in which topics unfold and roughly when, so you can read ahead and see where you are headed. It is not the authoritative calendar: exact dates, deadlines, and any mid-term adjustments live in Blackboard (the LMS), which is authoritative. A few fixed semester dates are noted below for orientation only — confirm everything against Blackboard.

The course runs fifteen weeks in five parts. Each part below lists its weeks, the theme for each week, and the throughline — the question that carries the week. The theme links to that week’s note.

Part I — Foundations of uncertainty (Weeks 1–2)

We lay the groundwork: what a probability is, how to model a random situation, and the basic rules that let you combine events.

Week	Theme	The throughline
1	Uncertainty, probability & models	What does it actually mean that the shuttle is on time with probability 0.81?
2	Sample spaces, events & rules	How do we list outcomes and combine events with the complement and addition rules?

Part II — Conditioning & Bayesian reasoning (Weeks 3–5)

The heart of the course: how a probability changes once you learn something, and how to invert that reasoning to update a belief.

Week	Theme	The throughline
3	Conditional probability	Why is being on time given rain (0.60) different from being on time overall (0.81)?
4	Independence & information	When does knowing one event tell you nothing about another — and when does it tell you a lot?
5	Bayes’ rule & updating	Given a positive screening test, how likely is the disease really?

Note: Labor Day (Mon, Sep 7) falls in Week 3 — no class that Monday, so the week is compressed to Wednesday and Friday.

Part III — Counting & discrete random variables (Weeks 6–9)

We learn to count outcomes systematically, then package randomness into discrete random variables and their standard models.

Week	Theme	The throughline
6	Counting & discrete probability	In how many ways can a 10-question true/false quiz come out, and what does that tell us?
7	Discrete random variables	How do we turn “number correct on the quiz” into a random variable with a distribution?
8	Expectation & variance	On average, how many do you get right by guessing, and how much does that vary?
9	Common discrete models	Which named models — binomial, Poisson — fit the quiz and the arriving shuttles?

Note: the midterm is Friday, Oct 9 (in class), during Week 7. No graded content appears on this public site — see Blackboard.

Part IV — Continuous variables & joint behavior (Weeks 10–12)

We move from counts to measurements, where probability becomes area under a density, and then to pairs of random quantities that vary together.

Week	Theme	The throughline
10	Continuous random variables	How long until the next shuttle — and why is probability now an area, not a sum?
11	Common continuous models	How do the exponential and normal models describe waiting and commute times?
12	Joint distributions & dependence	How do rain and lateness vary together, and what do covariance and correlation measure?

Part V — Limits, simulation & synthesis (Weeks 13–15)

We close with what happens in the large: how averages settle down and sums become bell-shaped, then a modeling project and a synthesis of the whole arc.

Week	Theme	The throughline
13	Sums, simulation & limit behavior	Why does the average commute time converge, and why does it become normal?
14	Probability modeling project	Can you build and defend a model of “at least 2 late days in a 5-day week”?
15	Final review & synthesis	How does the whole commuter’s-morning thread fit together as one picture?

Note: fall break is Nov 22–28 (no classes). The last class is Mon, Dec 7, and the final-exam window is Dec 9–15 (exact block via Blackboard).

Note

Again: this page is a pacing plan to help you read ahead. For the real calendar — every date, every deadline, and any adjustment during the term — Blackboard (the LMS) is authoritative.