Topological Data Analysis — Cohort site
Persistent homology, the Mapper algorithm, stability theorems, and what topological features add to a machine-learning pipeline.
Persistent homology, the Mapper algorithm, stability theorems, and what topological features add to a machine-learning pipeline.
| Level | Graduate / advanced undergraduate |
|---|---|
| Instructor | Dr. Yaé Ulrich Gaba |
| Meeting pattern | Mondays 14:00–16:00 lecture · Fridays 14:00–15:00 recitation (Africa/Lagos UTC+1) |
Prerequisites
Real analysis (sequences, continuity, compactness). Linear algebra. Basic probability. Python at the level of writing a 200-line script. No prior algebraic topology required — chapter 2 builds it from scratch.
Grading
Five problem sets (40%) · two code labs (20%) · paper-discussion recitations (10%) · final project (30%).
Reading
Co-authored The Shape of Data (No Starch Press), with Colleen Farrelly — recommended companion.
What this site is and isn’t
The bilingual notes (linked above) are the reference text. This cohort site is the operational layer: every week page has the lecture topic, the readings to do beforehand, the problem set or code lab, and any paper discussion. The schedule and weeks are generated from a single data file (_data/tda.yml), so the same source drives the landing, the schedule, and every week page. If you are reading along without being in a cohort, the week pages still work as a self-study guide; the deliverables become optional, but the readings and lecture topics are the same.