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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): Two-sided bound on sub-gaussian matrices
• Lecture 12 (Sept 21): Covariance Estimation and PCA
• Lecture 13 (Sept 26): Johnson-Lindenstrauss Lemma. Sparse Variant
• Lecture 14 (Sept 28): Fast JL Transform
• Lecture 15 (Oct 3): Applications of Johnson-Lindenstrauss 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): Sudakov-Fernique'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