Quentin Paris
HSE University
Consultation hours
Course
Send me an email to schedule an appointment online.
Modern Methods of Decision Making
Master Program

Data Science
Meetings

Mondays, from 11:10 to 14:20, from January 11 to June 14, 2021
Format

Online

Zoom link: here

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Telegram channel
Main references for the course:

S. ShalevShwartz and S. BenDavid (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

S. Bubeck (2015). Convex Optimization: Algorithms and Complexity. Foundations and Trends in Machine Learning. Vol. 8, No. 34.

E. Hazan (Book in Preparation). Introduction to Online Convex Optimization. Available here
Home assignments

Home assignement 3: Due April 26
Teaching material

Lecture notes, seminars, home assignments and video recordings: here
Previously:

Lecture 0: Introduction

Lecture 1: Statistical vs. online learning

Lecture 2: Tools from probability theory

Lecture 3: Recap on linear algebra and differential calculus

Lecture 4: Convexity

Lecture 5: Empirical risk minimisation I

Lecture 6: Empirical risk minimisation II

Lecture 7: Convex approach to binary classification

Lecture 8: Gradient descent I

Lecture 9: Gradient descent II
Next:

Lecture 10: Mirror descent (April 19)

Lecture 11: Stochastic optimisation

Lecture 12: Introduction to online learning

Lecture 13: Prediction with expert advice

Lecture 14: First order methods for online convex optimisation

Lecture 15: Online newton step algorithm

Lecture 16: Stochastic bandit algorithms

Lecture 17: Adversarial bandit algorithms

Lecture 18: Bandit convex optimisation
Course
Gradients flows in metric spaces
PhD Program
Meetings

Thursdays, from 18:10 to 21:00, from January 14 to March 18, 2021
Format

Online

Zoom link: here

pwd: come_in
Telegram channel
References for the course

S. Danieri and G. Savaré (2014). Lecture notes on gradient flows and optimal transport. In Y. Ollivier, H. Pajot, & C. Villani (Eds.), Optimal Transport: Theory and Applications (London Mathematical Society Lecture Note Series, pp. 100144). Cambridge: Cambridge University Press. (available on arxiv here)

L. Ambrosio, N. Gigli and G. Savaré (2005). Gradient Flows. Birkhauser.

L. Ambrosio and N. Gigli (2009). A user’s guide to optimal transport. (available here)
Teaching material

Lecture notes: coming soon
Previously

Lecture 0: Intro

Lecture 1: Review of gradient flows in Euclidean spaces

Lecture 2: Definitions of gradient flows in metric spaces: EDI, EDE and EVI

Lectures 3 and 4: Existence and uniqueness of gradient flows

Lecture 5: Optimal transport I
Next

Lecture 6: Optimal transport II (April 15)