Statistical Learning: Methodology and Theory
Class Schedule:
Lectures : T Th 11:45am-1pm at Old Chem 025
Office Hours : T 2-3pm, lounge beside 118 (I should be in 118A, please call me)
Quiz Schedule:
Q1 - September 21, 2017
Q2 - October 19, 2017
Q3 - November 28, 2017
On these days, we will have a 60min lecture, followed by a (15-20)min quiz
Mid-sem deadline for project : October 15, 2017 (extended)
Deadline for project : November 30, 2017
Project presentation : December 5 and 7, 2017 from 11:45am-1pm at Old Chem 025 with a 15min presentation by each student
Books:
Hastie, Tibshirani and Friedman
Pollard (go to Books)
Asymptotic Statistics by A. W. van der Vaart (check Duke library for a copy)
Statistics for High-Dimensional Data: Methods, Theory and Applications by Peter Bühlmann and Sara van de Geer
Assignments:
Notes:
Course Note 1 and 2 (Thanks to Xu Chen)
Course Note (Thanks to Jialiang Mao)
Consistency in Logistic Regression (also see in the 'Papers' below)
Useful Links:
Mahalanobis distance and Elliptic distributions
Sub-gradient (check example in Section 3.4)
Linear Maps and Orthogonal Projections
The Parzen window estimate of a pdf (thin black line) matches with the actual pdf (thicker blue line). The histogram of the actual data points are shown in light gray in the background.
Consistency of kNN (check p.2 onwards)
Proof of Stone's (1977) result
High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality
Papers:
The use of multiple measurements in taxonomic problems by R. A. Fisher
On the generalized distance in statistics by P. C. Mahalanobis
Logistic Regression: Consistency - I
Logistic Regression: Consistency - II
Best Subset Selection With $l_0$ penalty and Some comments
LASSO for Logistic Regression - I and II
Optimal Smoothing in Kernel Discriminant Analysis
On Error-rate Estimation in Nonparametric Classification
Loftsgaarden and Quesenberry (1965)
Geometric representation of HDLSS data
Coffee with everyone!