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 assignment submission:
1. This exercise has multiple parts. P
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 resampling methods:
cross-validation and the bootstrap.
The
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 rearrangement, control of plot sizes, the tikz device, gure capti
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
Email
Class Time
Room
Office Hours
Dr. Abbass Sharif
BRI 40
0
50
100
200
TV
300
25
5
10
15
Sales
20
25
20
15
Sales
5
10
15
5
10
Sales
20
25
What is Statistical Learning?
0
10
20
30
40
50
0
20
Radio
40
60
80
100
Newspaper
Shown are Sales vs TV, Radio and Newspaper, with a blue
linear-regression line t separately to
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 lectures
covering Chapter 7 of the text, we generalize
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 from its mistakes According to David
Ferrucci (PI of W
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 values in the set C, the classication task is to build
a fu
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 ways:
We soften what we mean by separates, and
We enrich
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!
But often the linearity assumption is good enough.
1 /
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 splitting rules used to segment the
predictor space ca
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 features
X1 , X2 , . . . , Xp for each object, as well as
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 between each part.
2. All relevant computer output should be
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 between each part.
2. All relevant computer output should
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 between each part.
2. All relevant computer output should b
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 between each part.
2. All relevant computer output should be
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 + to p xp
Linear regression is 0 + 1 x1 approach . . . s