---
title: Optimization for Data Sciences
author: Samuel Vaiter
date: 2026-09-09
status: in progress
---

## Schedule

Sept. 9: Introduction and Convex Analysis (S. Vaiter)

- [Lecture 1 notes](/a-first-look-at-convex-analysis/)
- [Lecture 1 (PDF)](https://files.samuelvaiter.com/notes/a-first-look-at-convex-analysis/a-first-look-at-convex-analysis.pdf)

Sept. 16 (morning): Gradient descent (S. Vaiter)

- [Lecture 2 notes](/following-the-steepest-direction/)
- [Lecture 2 (PDF)](https://files.samuelvaiter.com/notes/following-the-steepest-direction/following-the-steepest-direction.pdf)

Sept. 16 (afternoon): Automatic differentiation (S. Vaiter)

- [Lecture 3 notes](/automatic-differentiation/)
- [Lecture 3 (PDF)](https://files.samuelvaiter.com/notes/automatic-differentiation/automatic-differentiation.pdf)

Sept. 23: Conjugate gradient (A. Gramfort)

Sept. 30: Proximal methods (S. Vaiter)

## References

- [Convex Optimization by Boyd and Vandenberghe](https://web.stanford.edu/~boyd/cvxbook/).
- [Lecture Notes on Convex Optimization by Nesterov](https://shuyuej.com/books/Lectures%20on%20Convex%20Optimization.pdf).
- [Numerical Optimization by Nocedal and Wright](https://www.ime.unicamp.br/~pulino/MT404/TextosOnline/NocedalJ.pdf).
- [Optimization for Modern Data Analysis by Recht and Wright](https://people.eecs.berkeley.edu/~brecht/opt4ml_book/).