Welcome to CSI 772 / STAT 772
Lectures: Thursdays 4:30pm - 7:10pm, Acquia Hall 219
If you send email to the instructor,
please put "CSI 772" or "STAT 772" in the subject line.
``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
Calculus-level 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,
Springer-Verlag, 2006. ISBN 978-0-387-84857-0.
The website for the text is
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 1--4, 7, 9, 10, and 12--15.
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
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.
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)
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.
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
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.
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 cut-and-paste capabilities on webpages, it is easy to plagarize.
Sometimes it is even accidental, because it results from legitimate note-taking.
Some good guidelines are here:
See especially the entry "26 Guidelines at a Glance".
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.
The details of the schedule will evolve as the semester progresses.
Week 1, January 24
Course overview; notation; etc.
General methods of statistics: Decisions, models, linear regression, etc.
The R program.
Assignment 1, due January 31: In HTF exercises 2.1, 2.4, and 2.7, and
Week 2, January 31
Basic properties of random variables and probability.
Assignment 2, due February 7:
In HTF exercises 2.8, 3.1, 3.2, 3.4, 3.5, 3.6, and 3.7.
Week 3, February 7
Linear classification in R; the "vowel data''.
- variances of least squares estimators.
- variable selection in regression: least squares and ridge.
- model building: partial least squares, lasso, and LAR.
Assignment 3, due February 14: In HTF: exercises 3.9, 3.11, 3.19, 3.23, and 3.27.
Week 4, February 14
Assignment 4, due February 21: In HTF: exercises 4.2(a), 4.3, 4.5, 4.6(a),
Smoothing, overfitting, bias/variance tradeoff.
Criteria for comparing models.
Cp, AIC, BIC, CV, and bootstrap estimation of the prediction error in linear
Linear methods for classification: discriminant analysis, linear and quadratic.
Because the lecture did not cover enough material, this assignment will not
be turned in.
Week 5, February 21
Discuss previous assignments.
Linear methods for classification: discriminant analysis and logistic regression.
Assignment 5, due February 28: Access the "vowel data" and develop a
classifier from the training data using
(1) linear regression
(2) logistic regression
For each classifier, determine the error rate in the test data.
Week 6, February 28
Linear methods for classification: review and miscellaneous topics.
Project preliminary assignment, due March 21: Pick out two datasets in the
UCI 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 7
Midterm: mostly Chapters 1 through 4 and 7 in HTF.
Closed book, closed notes, and closed computers except for one sheet (front and back) of
Class does not meet.
Week 8, March 21
Additive models and trees
Assignment 6, due March 28: In HTF, read Sections 9.1-9.3.
Use a classification tree on the "vowel data" using the training data.
(There are different choices you can make for
your tree -- any are acceptable, but describe what you do.)
Determine the error rate in the test data
for your fitted tree.
Week 9, March 28
PRIM, MARS, HME, boosting
Assignment 7, due April 4:
In HTF exercise 9.2, 9.5(a)(b)(c)(d) for a regression tree, 10.4(a)(b) using a tree function in R
Week 10, April 4
More on trees: boosting, random forests
Reference: Ian Witten, Eibe Frank and Mark Hall (2011)
Practical Machine Learning Tools and Techniques
, third edition, Morgan Kaufmann Publishers (ISBN: 978-0-12-374856-0)
Assignment 8, due April 11:
Analyze the "vowel data".
In each case, develop a classifier using the training data and
determine the error rate in the test data
for your classifier. In each case, of course, there are choices you can make.
1. Use your implementation of AdaBoost that uses the tree function in R (Exercise 10.4 in HTF).
2. Use AdaBoost (AdaBoostM1) in Weka amd/or RWeka (your choice).
3. Use Random Forests (RandomForest) in Weka amd/or RWeka (your choice).
4. Write a brief summary comparing the methods.
Week 11, April 11
Support vector machines
Variations on LDA
Prototypes and nearest neighbors
Assignment 9, due April 18:
In HTF, read/skim Chapters 12 and 13. Work Exercises 12.1, 12.4(a), 12.9, 13.3.
Week 12, April 18
Review/discuss various issues in boosting
Review/discuss various issues in SVM
Nearest neighbors and unsupervised learning
Assignment 10, due April 25:
In HTF, read/skim Chapter 14. Work Exercise 14.1.
Week 13, April 25
Week 14, May 2
Projects due. We may spend some time in class discussing them.
4:30pm - 7:15pm Final Exam.
Closed books, notes, and computers.