This page is the practical orientation: the rhythm of each week, what the assessments look like, how to navigate the materials, and what to expect if you're a UCT student versus a self-learner reading along. For the bigger picture — who built this, why, and the licensing — see the About page.
The Weekly Rhythm
Each of the twelve weeks follows the same three-phase structure. The phases are designed to be done in order, but each one stands alone if you only have time for part of the week.
Pre-Class
Readings, short videos, and a few framing questions. This is where you build the conceptual ground for the week. Most weeks have 2–4 core readings plus optional supplementary material. All readings are openly accessible.
In-Class
Discussion, hands-on activities, worked examples, and group work. For UCT students this happens in the scheduled session; for self-learners, the activity prompts are written so you can run them yourself or with a study group.
Post-Class
Deeper reading, an assignment, or a reflective task. This is where the week's ideas get applied to your own research questions and where most of the assessment work happens.
Assessment
The course is graded across three components. The weighting is deliberately balanced so that no single deliverable dominates.
| Component | Weight | What it covers |
|---|---|---|
| Weekly practical exercises | 40% | Short, hands-on tasks tied to each week's content — verifying citations, running an analysis, auditing AI-generated text. Submitted progressively across the term. |
| Research enhancement project | 40% | An applied project using AI tools to support a piece of your own research, with a final presentation. Combines the practical skills built across the course. |
| Personal ethical framework | 20% | A written framework for your own use of AI in research, developed iteratively from Week 4 onwards and grounded in the ethical lenses introduced there. |
How to Navigate the Materials
The landing page lists every week and every sub-lesson in order. A few things worth knowing as you work through them:
- Core vs supplementary pages: sub-lessons marked (supplementary) in the contents list are deeper dives or adjacent topics. They are useful but not required for the assessments.
- Linear vs cherry-picked: the weeks build on each other — particularly Weeks 1–4 (foundations and ethics) which underpin everything that follows. Weeks 5–8 (literature, writing, code, multimodal) can be read in any order once you've done the foundations.
- Hands-on activities: every week ends with an "Activities and Assessment" page. Even if you're not formally enrolled, working through these is by far the most effective way to internalise the material.
- External links: all open in a new tab. Where readings are paywalled, we link to an open-access version (preprint, postprint, or institutional copy) wherever one exists.
Who This Page Is For
UCT students enrolled in MAM5020F
The authoritative versions of these materials, plus all assignments and grading, live on Amathuba. This site mirrors the lesson content for ease of reference and revision — treat Amathuba as the source of truth for deadlines and submissions.
Self-learners and other educators
Everything you need is on this site. The materials are released under CC BY 4.0, so you are welcome to read, adapt, and reuse them in your own teaching with attribution. See the About page for the suggested citation.
Prerequisites
None. The course is pitched at NQF Level 9 (postgraduate) but assumes no prior background in machine learning, computer science, or programming. What it does assume is that you are an active researcher or graduate student with a research project, problem, or question of your own to bring to the material.
The hands-on weeks (especially Weeks 5, 7 and 8) involve using AI tools directly. You will need a laptop, a free or paid account with at least one frontier AI assistant (Claude, ChatGPT, or Gemini), and a willingness to try things and break them.