STAT 689, STATISTICAL ASPECTS OF MACHINE LEARNING AND DATA MINING

STAT 689, STATISTICAL ASPECTS OF MACHINE LEARNING AND DATA MINING, FALL 2009

Instructor

Dr Marc G. Genton

Phone: 458-0889

Office: 502B Blocker

Office hours: Wed. 3-5pm.

Textbook

Izenman, A. J. (2008), Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer.

Web page for the book: astro.ocis.temple.edu/~alan/MMST

Other book

Hastie, T., Tibshirani, R., Friedman, J. (2008), The Elements of Statistic al Learning: Data Mining, Inference, and Prediction. Springer, Second Edition.

Web page for the book: www-stat-class.Stanford.EDU/~tibs/ElemStatLearn/

Course Schedule

Lecture: T R: 2:20-3:35PM in Blocker 411
Preq: STAT 610, 611

Course information ( .pdf )

R tutorial ( .pdf )

Topics

Sept. 1: Introduction (1)
Sept. 3: Data and Databases (2)
Sept. 8: Random Vectors and Matrices (3)
Sept. 10: Skew-Elliptical Distributions
Sept. 15: Model Assessment and Selection in Multiple Regression (5)
Sept. 17: Model Assessment and Selection in Multiple Regression (5)
Sept. 22: Model Assessment and Selection in Multiple Regression (5) *** 2.5 due Sept. 22 ***
Sept. 24: Model Assessment and Selection in Multiple Regression (5)
Sept. 29: Linear Discriminant Analysis (8)
Oct. 1: Linear Discriminant Analysis (8)
Oct. 6: Linear Discriminant Analysis (8) *** project proposal due ***
Oct. 8: NO CLASS
Oct. 13: Recursive Partitioning and Tree-Based Methods (9)
Oct. 15: Recursive Partitioning and Tree-Based Methods (9) *** 5.18 due Oct. 15 ***
Oct. 20: Support Vector Machines (11)
Oct. 22: Support Vector Machines (11)
Oct. 27: Support Vector Machines (11)
Oct. 29: Committee Machines (14)
Nov. 3: Committee Machines (14) *** 8.10 due Nov. 3 ***
Nov. 5: Committee Machines (14)
Nov. 10: Latent Variable Models for Blind Source Separation (15)
Nov. 12: Latent Variable Models for Blind Source Separation (15)
Nov. 17: Nonlinear Dimensionality Reduction (16) *** 14.7 due Nov. 17 ***
Nov. 19: Nonlinear Dimensionality Reduction (16)
Nov. 24: Artificial Neural Networks (10) *** draft project report due ***
Nov. 26: THANKSGIVING
Dec. 1: Project presentations: Y. Sun; L. Zhang; P. Pande; X. Zhu; S. Mukhopadhyay; G. Xu; M. Kim; H. Chen
Dec. 3: Individual project revision
Dec. 8: *** final project report due by 2pm sent by e-mail***

Homeworks

HW1: 2.4, 2.5, 3.6, 3.9, 3.20 *** 2.5 due Sept. 22 *** Solution from G. Xu
HW2: 5.4, 5.6, 5.7, 5.9, 5.18 *** 5.18 due Oct. 15 *** Solution from G. Xu
HW3: 8.3, 8.4, 8.10, 9.2, 9.6 *** 8.10 due Nov. 3 ***
HW4: 11.3, 11.6, 11.7, 11.12, 14.7 *** 14.7 due Nov. 17 ***