Reading material for the class will be assigned from:

[BLM] Concentration Inequalities: A Nonasymptotic Theory of Independencei, by S. Boucheron, G. Lugosi and P. Massart, Oxford University Press, 2013.
[V] High-Dimensional Probability, An Introduction with Applications in Data Science, by R. Vershynin, Cambridge University Press, 2018, available here.

Date	Lecture Topic	Readings
36-710, Fall 2018 Class Schedule
Aug 30, M	Introduction and recap from 709	[BLM] Sections 2.1 - 2.3
Sep 1, M	Recap from 709	[BLM] Sections 2.4 - 2.7
Sep 8, M	Recap from 709 - Jonson-Lindenstrauss Lemma	[BLM] Sections 2.8, 2.9
Sep 13, M	Orlicz norms	See references page.
Sep 15, W	Bennet-Orlicz norm and sub-Weibull Random variables	See references page.
Sep 20, M	Sub-Weibull Random variables and Efron-Stein Inequality	See references page. [BLM] Sections 3.1 - 3.4
Sep 22, M	Efron-Stein Inequality and Poincare' inequalities	[BLM] Sections 3.5 and 3.7
Sep 27, M	Entropy and the Transportation Lemma	[BLM] Sections 4.8, 4.19, 4.10
Sep 29, M	Applications of the Transportation Lemma: Pinsker's inequality and Adaptive Data Analysis	[BLM] Section 4.11 See references page.

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