Welcome to CSI 772
Statistical Learning
Spring, 2016
Instructor:
James Gentle
Lectures: Thursdays 4:30pm  7:10pm, Innovation Hall 133
If you send email to the instructor,
please put "CSI 772" in the subject line.
Course Description
``Statistical learning'' refers to analysis of data with the objective of
identifying patterns or trends. We distinguish supervised learning,
in which we seek to predict an outcome measure or class based on a sample
of input measures, from unsupervised learning,
in which we seek to identify and describe relationships and patterns among a sample
of input measures. The emphasis is on supervised learning, but
the course addresses the elements of both supervised learning and unsupervised
learning. It covers essential material for developing new statistical
learning algorithms.
Prerequisites
Calculuslevel probability and statistics, such as in CSI 672/STAT 652, and
some general knowledge of applied statistics.
Text and other materials
The text is T. Hastie, R. Tibshirani, and J. Friedman (HTF)
The Elements of Statistical Learning, second edition,
SpringerVerlag, 2009. ISBN 9780387848570.
The website for the text is
http://wwwstat.stanford.edu/ElemStatLearn/.
The course organization and content will closely follow that of the text.
The text is quite long, however, and so some topics will be covered very lightly,
and some whole chapters will be skipped completely. The main chapters we will
cover are 14, 7, 9, 10, and 1215. Also, we will discuss some methods that are
not covered in the text.
The software used in this course is R, which is a freeware package that can be
downloaded from the
Comprehensive R Archive Network (CRAN).
It is also available on various GMU computers in student labs.
No prior experience in R is assumed for this course.
A good site for getting started with R, especially for people who are somewhat
familiar with SAS or SPSS, is
Quick R.
Lectures
Students are expected to attend class and take notes as they see appropriate.
Lecture notes and slides used in the lectures will usually not be posted.
Grading
Student work in the course (and the relative weighting of this work
in the overall grade) will consist of
homework assignments, mostly exercises in the text (15)
project (15)
midterm exam (30)
final exam (40)
You are expected to take the final exam during the designated time period.
Incomplete grades will not be granted except under very special circumstances.
Homework
Each homework will be graded based on 100 points, and 5 points will be deducted
for each day that the homework is late, and will not be accepted if more than
5 days late (weekends count!).
Start each problem on a new sheet of paper and label it clearly.
Homework will not be accepted as computer files (and certainly not as
faxes!); it must be submitted on
paper.
Project
Each student must complete a project in the area of statistical learning.
The project will involve comparison of classification methods using
a dataset from the
University of California at Irvine (UCI) Machine Learning Repository.
Because the available time for the class is not sufficient to cover all of
even the most common methods of learning, a student may wish to do a project
involving methods addressed in the
text, but which are not covered in class.
The project will require a written report and, depending on available class
time, may involve an oral presentation.
Academic honor
Each student enrolled in this course must assume the
responsibilities of an active participant in GMU's scholarly
community in which everyone's academic work and behavior are
held to the highest standards of honesty. The GMU policy on
academic conduct will be followed in this course.
Make sure that work that is supposed to be yours is indeed your own
With cutandpaste capabilities on webpages, it is easy to plagarize.
Sometimes it is even accidental, because it results from legitimate notetaking.
Some good guidelines are here:
http://ori.dhhs.gov/education/products/plagiarism/
See especially the entry "26 Guidelines at a Glance".
Collaborative work
Students are free to discuss homework problems or other topics
with each other or anyone else, and are
free to use any reference sources. Group work and discussion outside of
class is encouraged, but of course explicit copying of homework solutions
should not be done.
Students with disabilities
Certification of a disability that requires accommodations must be
be made by the
Office of Disability Services (ODS).
If you are a student with a disability and desire academic accommodations,
please contact ODS and inform me during the first two week of classes.
All academic accommodations must be arranged through the ODS.
Approximate schedule
The details of the schedule will evolve as the semester progresses.
Week 1, January 21
 CSI 772
 Course overview; notation; etc.

Background in ``learning'' (``machine'', statistical'', ``computational'',...)

General methods of statistics: Decisions, models, linear regression, etc.

Simulation in R (and other comments about R).

Assignment 1, due
February 4: In HTF exercises 2.1, 2.4, and 2.7, and
Exercise 1.A.
In exercise 2.1, add "the norm is Euclidean."
Week 2, January 28
Week 3, February 4

Methods based on linear regression.
 variances of least squares estimators.
 variable selection in regression: least squares and ridge.
 model building: partial least squares, lasso, and LAR.
 Assignment 2, due February 11:
In HTF exercises 2.8, 3.1, 3.2, 3.4, 3.5, and 3.6.
You may wish to use the R function knn in the class
package.
Week 4, February 11

Comments on assignment 1.

Comments on optimization (and exercise 3.27).

Smoothing, overfitting, bias/variance tradeoff.

Criteria for comparing models.

Cp, AIC, BIC, CV, and bootstrap estimation of the prediction error in linear
regression models.

LAR (slide 12 from last week)

Linear methods for classification:
linear discriminant analysis.

Linear classification in R; the "vowel data''.

Assignment 3, due
February 25: In HTF: exercises 3.9, 3.11, 3.19, 3.23, and 3.28.
Week 5, February 18
Week 6, February 25
 Miscellaneous topics

Discuss project.
Project preliminary assignment, due March 17: Pick out two datasets in the
UCI Machine Learning Repository
that are appropriate for classification. For each, give the
name of the dataset, a one or two sentence general description, the list of
variables and their types, and the actual values of
the first observation.
Week 7, March 3
Midterm: mostly material from Chapters 1 through 3 in HTF.
Closed book, closed notes, and closed computers except for one sheet (front and back) of
prewritten notes.
March 10
Class does not meet.
Week 8, March 17
Week 9, March 24
Week 10, March 31
Week 11, April 7
General comments on fitting
(estimation, classification, prediction, smoothing, etc.)
More on trees:
Recent developments in neural nets
Week 12, April 14
Support vector machines (Chapter 12)
Week 13, April 21
Week 14, April 26
 Project presentations.
 Review.
Cinco de Mayo (May 5)
Final Exam.
4:30pm  6:30pm
Closed books, notes, and computers.