开源软件名称（OpenSource Name）：

epfml/OptML_course

开源软件地址(OpenSource Url)：

https://github.com/epfml/OptML_course

开源编程语言(OpenSource Language)：

Jupyter Notebook 81.6%

开源软件介绍(OpenSource Introduction)：

EPFL Course - Optimization for Machine Learning - CS-439

Official coursebook information

Lectures: Fri 13:15-15:00 in CO2

Exercises: Fri 15:15-17:00 in BC01

This course teaches an overview of modern mathematical optimization methods, for applications in machine learning and data science. In particular, scalability of algorithms to large datasets will be discussed in theory and in implementation.

Team

Instructors:
- Martin Jaggi [email protected]
- Nicolas Flammarion [email protected]
Assistants:
- Aditya Varre [email protected]
- Amirkeivan Mohtashami [email protected]
- Matteo Pagliardini [email protected]
- Scott Pesme [email protected]

Contents:

Convexity, Gradient Methods, Proximal algorithms, Subgradient Methods, Stochastic and Online Variants of mentioned methods, Coordinate Descent, Frank-Wolfe, Accelerated Methods, Primal-Dual context and certificates, Lagrange and Fenchel Duality, Second-Order Methods including Quasi-Newton Methods, Derivative-Free Optimization.

Advanced Contents:

Parallel and Distributed Optimization Algorithms

Computational Trade-Offs (Time vs Data vs Accuracy), Lower Bounds

Non-Convex Optimization: Convergence to Critical Points, Alternating minimization, Neural network training

Program:

Nr	Date	Topic	Materials	Exercises
1	25.2.	Introduction, Convexity	notes, slides	lab01
2	4.3.	Gradient Descent	notes, slides	lab02
3	11.3.	Projected Gradient Descent	notes, slides	lab03
4	18.3.	Proximal and Subgradient Descent	notes, slides	lab04
5	25.3.	Stochastic Gradient Descent, Non-Convex Optimization	notes, slides	lab05
6	1.4.	Non-Convex Optimization, Accelerated Gradient Descent	notes, slides	lab06
7	8.4.	Newton's Method & Quasi-Newton	notes, slides	lab07
.	15.4.	`easter vacation`		-
.	22.4.	`easter vacation`		-
8	29.4.	Coordinate Descent	notes, slides	lab08
9	6.5.	Frank-Wolfe	notes, slides	lab09
10	13.5.	Accelerated Gradient, Gradient-free, adaptive methods	notes, slides	lab10
11	20.5.	Opt for ML in Practice	notes, slides	Q&A
12	27.5.	`Mini-Project week`		-
13	3.6.	Opt for ML in Practice	notes, slides	Q&A Projects

Videos:

Exercises:

The weekly exercises consist of a mix of theoretical and practical Python exercises for the corresponding topic each week (starting week 2). Solutions to theory exercises are available here, and for practicals in the lab folder.

Project:

A mini-project will focus on the practical implementation: Here we encourage students to investigate the real-world performance of one of the studied optimization algorithms or variants, helping to provide solid empirical evidence for some behaviour aspects on a real machine-learning task. The project is mandatory and done in groups of 3 students. It will count 25% to the final grade. Project reports (3 page PDF) are due June 17th. Here is a detailed project description.

Assessment:

Final written exam in exam session on Thursday 07.07.2022 from 09h15 to 12h15 (in CE1, CE1106, CE3) Format: Closed book. Theoretical questions similar to exercises. You are allowed to bring one cheat sheet (A4 size paper, both sides can be used). For practice: exam 2020, solutions 2020, exam 2019, solutions 2019, exam 2018, solutions 2018.