After (or sometimes before) lectures, we will write a blurb on what we did and provide references
to where the material is from. Sometimes we may provide pdfs of rough notes.

Lecture 1 (Aug 10): Basic properties of random variables and an application in geometry. Chapters 0 and 1 in
RV.

Lecture 2 (Aug 17): Gaussian, subgaussian and subexponential random variables. Chapter 2 in
RV and Chapter 1 in
PR

Lecture 3 (Aug 22): Random vectors in high dimensions. Chapter 3 in
RV

Lecture 4 (Aug 24): Linear Regression

Lecture 5 (Aug 29): Linear Regression continued

Lecture 6 (Aug 31): Constrained Linear Regression

Lecture 7 (Sept 5): L0 Constrained Linear Regression

Lecture 8 (Sept 7): No Class

Lecture 9 (Sept 12): Matrix Concentration

Lecture 10 (Sept 14): Community Detection in Block Stochastic Model

Lecture 11 (Sept 19): Twosided bound on subgaussian matrices

Lecture 12 (Sept 21): Covariance Estimation and PCA

Lecture 13 (Sept 26): JohnsonLindenstrauss Lemma. Sparse Variant

Lecture 14 (Sept 28): Fast JL Transform

Lecture 15 (Oct 3): Applications of JohnsonLindenstrauss Lemma

Lecture 16 (Oct 5): Gaussian Width and Gordon's Theorem

Lecture 17 (Oct 10): Lipschitz Concentration

Lecture 18 (Oct 12): Gordon's Escape Theorem and Compressed Sensing

Lecture 19 (Oct 17): No Class

Lecture 20 (Oct 19): No Class

Lecture 21 (Oct 24): Gaussian processes and Slepian's inequality

Lecture 22 (Oct 26): SudakovFernique's inequality and sharp bounds on the norm of gaussian matrices

Lecture 23 (Nov 2): Concentration for gaussian processes and Sudakov's minorization inequality

Lecture 24 (Nov 7): Dudley's inequality and generic chaining

Lecture 25 (Nov 9): Talagrand's majorizing measure theorem

Lecture 26 (Nov 14): Empirical Processes and VC dimension

Lecture 27 (Nov 16): Empirical Processes and VC dimension continued

Lecture 28 (Nov 21): Class Presentations 1

Lecture 29 (Nov 23): Class Presentations 2