Foundations of Machine Learning — Cohort site
The classical machine-learning toolkit — linear and tree-based methods, SVMs, ensembles, clustering, dimensionality reduction, Bayesian learning — with the statistical learning theory underneath.
The classical machine-learning toolkit — linear and tree-based methods, SVMs, ensembles, clustering, dimensionality reduction, Bayesian learning — with the statistical learning theory underneath.
| Level | Advanced undergraduate / early graduate |
|---|---|
| Instructor | Dr. Yaé Ulrich Gaba |
| Meeting pattern | Tuesdays + Thursdays, 14:00–16:00 lecture · Fridays 14:00–15:00 problem-set review (Africa/Lagos UTC+1) |
Prerequisites
Linear algebra, calculus (gradients), undergraduate probability (random variables, expectation, conditional probability). Python at the level of writing and debugging a 200-line script.
Grading
Six problem sets (45%) · four code labs (25%) · final project (30%).
Reading
Hastie, Tibshirani, Friedman, The Elements of Statistical Learning (2nd ed.) — required reference. Murphy, Probabilistic Machine Learning — 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/apprentissage-automatique.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.