Consultation hours

Send me an email to schedule an appointment online.
 

Course

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
Main references for the course:
  •  S. Shalev-Shwartz and S. Ben-David (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. 3-4.
Home assignments
  • Home assignment 1: here (due February 26)
  • Home assignment 2: coming soon
Previously:
  • Video recordings: here
  • Lecture 0: Introduction (January 11)
  • Lecture 1:  Statistical vs. online learning  (January 18). 
  • Lecture 2: Tools from probability theory (January 25)
  • Lecture 3:  Recap on linear algebra and differential calculus  (February 1st)
  • Lecture 4: Convexity (February 8)
  • Lecture 5: Empirical risk minimisation I (Feb 15)
  • Lecture 6: Empirical risk minimisation II (March 1)
    • Notes: here
    • No seminar
Next: 
  • Lecture 7: Support vector machines (March 8)
  • Lecture 8: Boosting
  • Lecture 9: Gradient descent
  • Lecture 10: Mirror descent
  • 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

High dimensional probability and statistics
Master Program 
  • Statistical Learning Theory
Meetings
  • Wednesdays, from 13:00 to 16:00, from January 27 to March 17, 2021
Format
  • Online
  • Zoom link: here
Main references for the course:
  •  R. Vershynin (2018). High-Dimensional Probability. An Introduction with Applications in Data Science. Cambridge University Press.
  • M. Wainwright (2019). High-Dimensional Statistics. A Non-Asymptotic Viewpoint. Cambridge University Press.
Home assignments​
  • First: here (due February 24)
Previously: 
  • Video recordings: here
  • Lecture 1: Concentration (January 27)
  • Lecture 2: Sums of independent random variables (February 3)
  • Lectures 3 & 4: Suprema (February 10 & 17)
  • Lecture 5: The Johnson-Lindenstrauss lemma (February 24)
    • Notes: here
  • Lecture 6: Cov. matrix estimation and PCA (March 3)
    • Notes: coming soon
Next
  • Lecture 7: Concentration of random matrices (March 10)
  • Lecture 8: Community detection in random graphs (March 17)
  • Lecture 9: High-dimensional linear regression (March 24)

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
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. 100-144). 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
Previously
  • Lecture 0: Intro 
  • Lecture 1: Review of gradient flows in Euclidean spaces 
    • Notes: here             
  • Lecture 2: Definitions of gradient flows in metric spaces: EDI, EDE and EVI
  • Lecture 3: Minimizing movement scheme and existence of EDI gradient flows.
    • Notes: coming soon
Next
  • Lecture 4