Lecture number  Day  Topics 
 Jan 9   NO CLASS

1  Jan 14   Course logistics
 Linear model with n << p, sparsity
 Lasso

2  Jan 16   l1 regularized logistic regression
 GLMs with l1 regularization
 l1 SVMs, margins
 Matrix completion and trace norm

 Jan 21   NO CLASS (MLK Jr. Day)

3  Jan 23   1bit matrix completion
 Nonnegative matrix completion
 Covariance and concentration/precision matrix estimation

4  Jan 28   Gaussian graphical model selection using l_1 regularized log det
 Gaussian graphical model selection using parallel l_1 regularized regressions

5  Jan 30   High dimensional Ising model selection
 Sparse PCA: ScoTLASS, SDP relaxation of l_0 constrained PCA

6  Feb 4   Sparse PCA: Generalized power method

7  Feb 6   High dimensional kmeans

8  Feb 11   Sparse subspace clustering
 HW 1 out

9  Feb 13   Lasso l_2 error bounds for the linear model case
 Restricted eigenvalue (RE) condition
 Initial project proposals due (deadline extended till Feb 15)

10  Feb 18   Sup (i.e. l_\infty) norm error bounds and sign consistency of lasso
 Mutual incoherence condition

11  Feb 20   Sup norm error bound continued
 When does the RE condition hold with high probability?

12  Feb 25   Proximal methods
 Examples of prox operators
 HW 1 due

13  Feb 27   Convergence rates for proximal methods
 Final project proposals due

 Mar 4   NO CLASS (Spring break)

 Mar 6   NO CLASS (Spring break)

14  Mar 11   Coordinate descent methods
 HW 2 out

15  Mar 13   Least Angle Regression (LARS)

16  Mar 18   LARS: Lasso modification
 Estimation of high dimensional low rank matrices

17  Mar 20   Estimation of low rank matrices: Decomposition lemma for error matrix

18  Mar 25   Estimation of low rank matrices: Restricted Strong Convexity (RSC)
 HW 2 due
 HW 3 out

 Mar 27   NO CLASS
 Attend Prof. Andrew Gelman’s talk: “Causality and Statistical Learning” in the Ford School of Public Policy

19  Apr 1   Bound on the maximum singular value of a matrix with iid (multivariate normal) rows
 Gordon’s Theorem
 HW 3 due

20  Apr 3   Proof of Gordon’s theorem using Slepian’s inequality
 Gaussian concentration inequality for Lipschitz functions
 HW 4 out

21  Apr 8   Fisher’s LDA in high dimensions

22  Apr 10   Naive Bayes or Independence Rule in high dimensions
 HW 4 due

23  Apr 15   Loss based classification in high dimensions

 Apr 17   Project presentations I
 Hossein
 Yuan
 Can
 Robert
 Phoenix

 Apr 22   Project presentations II
 Kam, Yiwei
 Sougata
 Naveen
 Chia Chye Yee
 Xuan

 Apr 26   Project reports due
