IOM 530: Applied Modern Statistical Learning Methods
Assignment 1 (Due Sep 5, 2013)
Questions:
Exercise 8 in Chapter 2 on page 54 of An Introduction to Statistical Learning in R.
Guidelines for assign
Cross-validation and the Bootstrap
In the section we discuss two resampling methods:
cross-validation and the bootstrap.
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Cross-validation and the Bootstrap
In the section we discuss two resam
knitr Graphics Manual
Yihui Xie
September 28, 2013
This manual shows features of graphics in the knitr package (version
1.5) in detail, including the graphical devices, plot recording, plot rearrangem
UNIVERSITY OF SOUTHERN CALIFORNIA
MARSHALL SCHOOL OF BUSINESS
DATA SCIENCES AND OPERATIONS DEPARTMENT
FALL 2013
IOM 530 APPLIED MODERN STATISTICAL LEARNING METHODS
COURSE DETAILS
Professor
Office
Emai
Linear Model Selection and Regularization
Recall the linear model
Y = 0 + 1 X1 + + p Xp + .
In the lectures that follow, we consider some approaches for
extending the linear model framework. In the
Statistical Learning
Trevor Hastie and Robert Tibshirani
Statistics in the news
How IBM built Watson, its Jeopardy-playing
supercomputer by Dawn Kawamoto DailyFinance
02/08/2011
Learning
Classication
Qualitative variables take values in an unordered set C,
such as:
eye color cfw_brown, blue, green
email cfw_spam, ham.
Given a feature vector X and a qualitative response Y
taking valu
Support Vector Machines
Here we approach the two-class classication problem in a
direct way:
We try and nd a plane that separates the classes in
feature space.
If we cannot, we get creative in two way
Moving Beyond Linearity
The truth is never linear!
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Moving Beyond Linearity
The truth is never linear!
Or almost never!
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Moving Beyond Linearity
The truth is never linear!
Or almost never!
Tree-based Methods
Here we describe tree-based methods for regression and
classication.
These involve stratifying or segmenting the predictor space
into a number of simple regions.
Since the set of
Advanced Data Analysis & Data
Mining
Statistics 4850G/9850B/9851B
Summary Course Outline
Instructor, teaching assistant, location
Instructor:
A. I. McLeod
Teaching Assistant:
Ken Jinkun Xiao
Time & Lo
Unsupervised Learning
Unsupervised vs Supervised Learning:
Most of this course focuses on supervised learning methods
such as regression and classication.
In that setting we observe both a set of fe
IOM 530: Applied Modern Statistical Learning Methods
Assignment 3 (Due 9/26/2008)
Guidelines for assignment submission:
1. Type each question before you answer it, and provide a clear separation betwe
IOM 530: Applied Modern Statistical Learning Methods
Assignment 4 (Due Oct 3, 2013)
Guidelines for assignment submission:
1. Type each question before you answer it, and provide a clear separation bet
IOM 530: Applied Modern Statistical Learning Methods
Assignment 5 (Due 10/10/2008)
Guidelines for assignment submission:
1. Type each question before you answer it, and provide a clear separation betw
IOM 530: Applied Modern Statistical Learning Methods
Assignment 2 (Due 9/19/2008)
Guidelines for assignment submission:
1. Type each question before you answer it, and provide a clear separation betwe
Linear regression
Linear regression is a simple approach to supervised
learning. It assumes that the dependence of Y on
X1 , X2 , . . . Xp is linear.
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Linear regression
(x) = a simple + 2 x2 +