Location Olin 213
Course calendar
| Date | Topic and materials |
|---|---|
| Wed · Sep 2 | Introductions, syllabus, and software setupFor next time: Review the syllabus, obtain the textbook and read Module 1.1, and finish installing R and Positron if needed. |
| Fri · Sep 4 | R fundamentals: variables, vectors, and graphsFor next time: Make sure R and Positron are installed, and spend a little time experimenting with R. |
| Mon · Sep 7 | No class — Labor Day |
| Wed · Sep 9 | R fundamentals: loops and decisions |
| Fri · Sep 11 | Module 2.2: Unconstrained Growth and Decay — from rates to an R simulationFor next time: Read Module 2.2, “Unconstrained Growth and Decay.” |
| Mon · Sep 14 | Module 2.2: Experimenting with growth and decay — exact solutions, time-step error, and model-breaking testsAssigned: Project 1 · Saving for a Car. |
| Wed · Sep 16 | Project 1 walkthrough and using unconstrained growth and decay to answer threshold questions |
| Fri · Sep 18 | Module 2.3: Constrained Growth — carrying capacity, balancing feedback, and logistic growthFor next time: Read Module 2.3, “Constrained Growth.” |
| Mon · Sep 21 | Growth Models in Practice — trout harvesting and additional problems from Modules 2.2 and 2.3 |
| Wed · Sep 23 | Module 4.1: Competition — interacting populations and the shark competition modelFor next time: Read Module 4.1, “Competition.”Project 1 due: 11:59 p.m. on Moodle. |
| Fri · Sep 25 | Module 4.2: Predator–Prey Models — completed squirrel–hawk interactions, coupled Euler updates, population cycles, and phase plotsFor next time: Read Module 4.2, “Predator–Prey Models.”Assigned: Project 2 · Rabbit–Fox Dynamics. |
| Mon · Sep 28 | Module 4.3: The SIR Model — infection and recovery, outbreak peaks, and transmission reductionFor next time: Read Module 4.3, “Modeling the Spread of SARS—Containing Emerging Disease.” |
| Wed · Sep 30 | Module 5.2: Errors — where modeling and numerical errors come from, and how to measure themFor next time: Read Module 5.2, “Errors.” |
| Fri · Oct 2 | Module 5.2: Errors continued — normalized notation, precision, magnitude, overflow, and time-step approximation; Exam 1 study guide review |
| Mon · Oct 5 | Exam 1 review — what to expect, questions about the covered material, and worked study-guide problemsFor Friday: Use the study guide to practice before Exam 1. |
| Wed · Oct 7 | Module 6.2: Euler’s Method — why the update works, tangent-line estimates, and time-step error; this new material is not on Friday’s examProject 2 due: 11:59 p.m. on Moodle.For Friday: Review the Exam 1 study guide. |
| Fri · Oct 9 | Exam 1 — in class |
| Mon · Oct 12 | Module 6.3: Runge-Kutta 2 — predict with Euler, correct using the average rate, and compare numerical errorAssigned: Project 3 · Cooling a Drink.For next time: Read Module 6.3, “Runge-Kutta 2 Method.” |
| Wed · Oct 14 | Module 6.4: Runge-Kutta 4 — four trial changes, one weighted update, and a comparison with Euler and RK2For next time: Read Module 6.4, “Runge-Kutta 4 Method.” |
| Fri · Oct 16 | Numerical Methods in Practice — compare Euler, RK2, and RK4 on constrained growth; test whether the method and time step change a threshold predictionFor next time: Review Modules 6.2–6.4 and keep working on Project 3. |
| Mon · Oct 19 | Module 9.2: Monte Carlo Simulations — compare runif() and rnorm(), then estimate π with random darts and investigate why estimates varyFor next time: Read Module 9.2, “Simulations,” and keep working on Project 3. |
| Wed · Oct 21 | Module 9.5: Random Walk — turn random directions into one path, then repeat the simulation to estimate a typical displacement and the chance of ending near the start; time for Project 3 questionsFor next time: Read Module 9.5, “Random Walk.”Project 3 due: 11:59 p.m. on Moodle. |
| Fri · Oct 23 | No class — Fall Academic Holiday |
| Mon · Oct 26 | Module 8.3: Empirical Models — fit lines and curves to NIST data with lm(), read residuals, straighten a curve with a power transformation, and see why a perfect fit can be the wrong modelFor next time: Read Module 8.3, “Empirical Models.” |
| Wed · Oct 28 | Empirical Models in Practice — choose a function shape, fit a leveling-off model to the BOD data, test three models on data they haven’t seen, and measure how Euler, RK2, and RK4 errors shrinkFor next time: Review Module 8.3, “Empirical Models.” |
| Fri · Oct 30 | — |
| Mon · Nov 2 | — |
| Wed · Nov 4 | — |
| Fri · Nov 6 | No class — Everything Fab Four Fest: REVOLVER |
| Mon · Nov 9 | — |
| Wed · Nov 11 | — |
| Fri · Nov 13 | — |
| Mon · Nov 16 | — |
| Wed · Nov 18 | — |
| Fri · Nov 20 | — |
| Mon · Nov 23 | No class — Thanksgiving Holiday |
| Wed · Nov 25 | No class — Thanksgiving Holiday |
| Fri · Nov 27 | No class — Thanksgiving Holiday |
| Mon · Nov 30 | — |
| Wed · Dec 2 | — |
| Fri · Dec 4 | — |
| Mon · Dec 7 | — |
| Wed · Dec 9 | — |
| Fri · Dec 11 | — |
Final examinations
NoteOfficial exam times
Final examinations follow the College’s published Fall 2026 exam schedule:
- Section A · 10:30 a.m. class: Thursday, December 17 · 8:00–10:30 a.m.
- Section B · 1:00 p.m. class: Monday, December 14 · 3:00–5:30 p.m.