Syllabus

Public version. This page is the sanitized, public-facing summary of the syllabus. The full course syllabus — with section numbers, calendar dates, room assignment, and institution-specific policies — lives in the Assignments/LMS space.

What the course is about

Intro to Mathematical Software is a conference-based studio course in modern mathematical and computational workflow. It is not a traditional lecture/lab course. Most instruction lives on this public site; scheduled Mon/Wed class times are reserved for required checkpoint conferences, optional demonstrations, open studio work, troubleshooting, and individualized help.

The course is built around four interlocking pillars:

  1. LaTeX / mathematical writing — typesetting mathematics, theorem and proof blocks, figures, citations, polished PDFs.
  2. R / computation, visualization, simulation, and reporting — wrangling, ggplot2, simulation, reproducible reports.
  3. Technical workflow — Quarto, project organization, reproducibility, portfolio building, optional light Git/GitHub.
  4. Responsible AI-assisted technical work — disclosure, verification, revision, and critique. Grounded in published frameworks and peer-reviewed work — see the AI reading spine.

The course is AI-native, not AI-only. AI assistants are legitimate first-pass helpers for explanation, debugging, drafting, syntax lookup, and workflow support. Accountability comes from rendered documents, executable code, official documentation, reproducible organization, AI Use Notes, and instructor conferences.

Optional second-tool exploration in Week 13: a short look at a second computational tool — SageMath / SageMathCell, Python + SymPy, MATLAB / Octave, or a CAS option such as Maple. See CAS options.

Learning outcomes

By the end of the term, students should be able to:

  • Typeset mathematics, theorem/proof blocks, figures, tables, and bibliographies using LaTeX, and produce polished PDFs.
  • Import, wrangle, visualize, summarize, and report on data in R, using VS Code as the standard editor.
  • Build Quarto reports that combine prose, mathematics, code, output, figures, and citations.
  • Write simple simulations with reproducible seeds and interpret the output.
  • Organize project folders so technical work is reproducible, readable, and easy to revise.
  • Use AI assistants for debugging, drafting, explanation, and revision while verifying the output and disclosing the assistance.
  • Assemble a final portfolio that communicates growth across mathematical writing, computation, workflow, and responsible AI use.

Course format and attendance

This is a conference-based studio course meeting in a hybrid Mon/Wed time slot. The scheduled class times are not ordinary required class meetings. They are protected time for the five required checkpoint conferences, plus optional demos, troubleshooting, open studio, and individual help. Weekly deliverables create the pacing of the course whether or not you attend any optional support time.

In-person support happens in the Math Assistance Center (MAC); online students participating through the hybrid format complete the same weekly modules and conferences by the announced remote method.

Required conferences

Five short one-on-one conferences (10–15 minutes each) are scheduled across the semester. They are practical progress checks, not oral exams, and they are structured around the work you are already producing.

  • Week 1 — Setup conference (required). Confirm your software stack, portfolio folder, and first Quarto render.
  • Week 4 — LaTeX checkpoint conference (required). Confirm that core LaTeX skills are working; review figures/tables/bibliography workflow; check initial LaTeX Project direction while scope can still be adjusted.
  • Week 7 — R transition conference (required). Confirm portfolio is organized after the LaTeX module; confirm VS Code, R, and Quarto are working together; preview the R visualization and simulation sequence.
  • Week 10 — R Project conference (required). Confirm project track, dataset or simulation plan, and early report structure before submission.
  • Week 13 — Portfolio-workflow conference (required). Review portfolio organization, AI Use Notes, workflow habits, and final-reflection plan before final assembly and submission.

Conference sign-ups are handled through the course LMS.

Portfolio model

Across the semester you build a small portfolio of mathematical software artifacts. Portfolio organization is required; public posting is optional. The portfolio may live as a local folder, a private repository, or a public repository if you specifically choose to make your work public. Public GitHub is never required.

By the end of the term, your portfolio should include:

  • A selection of weekly assignment artifacts (mostly Quarto documents).
  • A LaTeX Project — a polished short paper.
  • An R Project — a Quarto-rendered data analysis or simulation report.
  • AI module artifacts from the AI module weeks (Weeks 11–12) — a debugging audit and an AI Use Reflection.
  • Optionally, a one-page bridge mini-report from the Week 13 second-tool exploration.
  • A 1–2 page final reflection.

Data and software

You will use:

  • VS Code, R, Quarto, and TinyTeX. VS Code is the course’s standard editor; R is the primary computation language. See Software setup and Lab 1.
  • Datasets from one of four sources: built-in R datasets, simulated data, public datasets with verifiable provenance and license, or instructor-approved student-selected datasets. See Data guidelines.

Final assessment

The final assessment is a portfolio plus a 1–2 page written reflection, submitted in Week 15 through the course LMS. There is no required final demo and no required Week 15 conference. The Week 13 portfolio-workflow conference is the structured closing checkpoint.

Grading

Work is evaluated across four categories with these weights:

Category Weight
LaTeX / mathematical writing 25 %
R / computation, visualization, simulation, and reporting 30 %
Technical workflow — Quarto, organization, reproducibility, portfolio, optional Git/GitHub 25 %
Generative AI literacy — responsible use, verification, disclosure, critique 20 %

The five required conferences are graded inside the category they belong to rather than as a separate category.

Weekly drop policy. The best 9 of 11 weekly assignments count for ordinary weekly-work credit. The two dedicated AI module assignments (Weeks 11 and 12) sit in the Generative AI Literacy category and are not droppable. Projects, required conferences, AI verification components, and the final portfolio are also not droppable. The drop policy is meant to absorb ordinary disruptions like illness, travel, or one difficult week — not to make any structural piece optional.

Grade scale. A: 90–100; B: 80–89; C: 70–79; D: 60–69; F: < 60.

Late work

Weekly assignments are accepted up to one week past the due date with a flat 20 % late penalty; after one week no credit is given unless the absence was excused.

Projects, required conferences, and the final portfolio require instructor coordination and may have separate extension rules. Coordinate before the deadline rather than after.

AI policy (summary)

You are encouraged to use generative AI assistants — ChatGPT, Claude, Copilot, Cursor, Gemini, etc. — as study partners, debugging helpers, drafting aids, and syntax lookup tools. You are responsible for the correctness, originality, and citation of the work you submit.

Every submission where AI was used must include a short AI Use Note with three labeled lines:

  • Tool — which assistant (and approximate date/version if known).
  • Purpose — what you used it for.
  • Verification — how you checked, tested, revised, or validated the output.

The verification line is the point. Using AI is not enough; you have to verify and take responsibility for the final work. You should be able to explain anything you submit, including code, mathematical notation, figures, and written interpretations.

Privacy. Do not paste other students’ work, non-public datasets, identifiable personal data, or LMS-only content into an AI tool.

Detectors. This course does not use automated AI-detection tools. What matters instead is a clear AI Use Note, work you can explain, and visible verification.

Full guidelines and the published sources:

Where things live

The public course site is the main home for notes, labs, examples, software setup, AI guidelines, and data guidelines — read it the way you would read a course text. The course LMS (Blackboard for Fall 2026) is the official place for assignment prompts, rubrics, submissions, the gradebook, announcements, conference sign-ups, and section-specific due dates.

If anything on this page conflicts with the course LMS, the LMS version wins.

Other policies

  • Academic integrity. Standard institutional policy applies. Pair it with the AI use guidelines: cite tools, cite sources, do not submit work you cannot explain.
  • Accessibility, weather, university policies. Linked from the course LMS syllabus. Anything binding lives there.