Syllabus
and
Schedule 
Course Information 

Textbooks and Software  Course
Policies 
Additional
Resources 
Wk  Date  Topic  Reading  Lab  Assignments  Comments 

1 
1/19 
Introduction to Machine Learning 
Alpaydin Ch.
1 
No lab 
kNN notes 

2 
1/24 
Statistical
Learning,
Empirical
Methodology,
Decision
Trees 
Alpaydin
Ch.
2 Mitchell Ch. 3 
Weka tutorial 
Assignment
1
out 

1/26 

3  1/31 
Review of Linear Algebra, Linear Discriminants  Alpaydin 10.110.7  Matlab tutorial 

2/2 
Linear methods for classification and regression  Optimization notes 

4  2/7 
Support Vector Machines and Kernel Methods  Bennett article  No
lab 
Assignment 1 due Assignment 2 out 
SVM notes ionosphere.data ionosphere.names 
2/9 
No
Class

Eric
out
of
town 

5  2/14 
SVMs
& Kernel Methods (continued) 
Alpaydin
13.113.7 
Probability
review 

2/16 
Generative
Modeling, Probability Review 
Alpaydin Appx. A, 3.13.5, 4.14.4  
6  2/21 
Bayes
Rule,
Graphical
Models, Basic Bayesian Nonparametrics Logistic Regression 
Alpaydin 4.54.8, 16.116.3  Assignment 2 due Assignment 3 out 

2/23 
Alpaydin
8.18.4 

7  2/28 
Logistic Regression (continued),
MLE
and
MAP Ensemble Methods 
Alpaydin
5.15.5 Alpaydin 10.7 

3/2 
Alpaydin
12.812.9,
17.117.4, 17.617.9 

8  3/7 
No
Class  Spring Break 

3/9 

9  3/14 
Exam 1 
Assignment
3
due 
Exam 1 Equation Sheet 

3/16 
Semisupervised and Unsupervised Learning  Alpaydin
7.17.4 
Assignment 4
out 

10  3/21 
Semisupervised and Unsupervised Learning (continued)  Alpaydin
7.57.8 

3/23 
Relational
Learning
Primer 
Jen Neville's visit on Wednesday  
11  3/28 
Semisupervised
learning
for
text and image classification 
Tutorial on Feature
Learning for Images: Part
0, Part
1 

3/30 
Transfer and Lifelong Learning  Assignment 4
due Project Description 

12  4/4 
Transfer
Learning
(continued) / Dimensionality Reduction 
Alpaydin
6.16.8 

4/6 
Dimensionality Reduction, Manifold Methods  Dimensionality Reduction
Notes 

13  4/11 
Manifold
Methods / Isomap 

4/13 
Reinforcement Learning and MDPs  Sutton & Barto  RL Notes  
14 
4/18 
Reinforcement Learning and MDPs  Sutton
&
Barto 

4/20 

15 
4/25 
Adv.
Topics / Review 

4/27 
Exam 2 

TBA 
Project Presentations during Final Exam
slot 
Final
Project Due 

Lecture
Hours:
Mondays, Wednesdays, 10:0011:30 am
Room: Park 230
Lab
Hours: Wednesday 24pm
Lab Room: Park 232
Website: http://cs.brynmawr.edu/cs380/
Professor:
Eric Eaton
Office: Park 249
Office Phone: 6105265061
EMail:
Website: http://cs.brynmawr.edu/~eeaton
Email
is the best way to reach me, and I make a concerted effort to respond
to all emails within 24 hours on weekdays and 48 hours on weekends
(often, much less!).
Office
hours: 12pm Tuesday/Thursday and by appointment
Communication
Attendance
and
active
participation
are
expected
in
every
class.
Participation
includes
asking
questions,
contributing
answers,
proposing
ideas,
and
providing
constructive
comments.
As you will discover, I am a proponent of twoway communication and I
welcome feedback during the semester about the course. I am available
to answer student questions, listen to concerns, and talk about any
courserelated topic (or otherwise!). Come to office hours! This helps
me get to know you. You are welcome to stop by and chat. There are many
more exciting topics to talk about that we won't have time to cover
inclass.
Whenever you email me, please use a meaningful subject line and
include the phrase "CS380" at the beginning of that line. Your email
will catch my attention and I will respond quicker if you do this. I
make an effort to respond to emails within 24 hours on weekdays and 48
hours on weekends.
Although computer science work can be intense and solitary, please stay
in touch with me, particularly if you feel stuck on a topic or project
and can't figure out how to proceed. Often a quick email, phone call
or facetoface conference can reveal solutions to problems and
generate renewed creative and scholarly energy. It is essential that
you begin homeworks and projects early, since we will be covering a
variety of challenging topics in this course.
Grading
At the end of the semester, final grades will be calculated
as a
weighted average of all grades according to the following weights:
Homeworks

40%
(10%
each)
Exam 1  15%
Exam 2  15%
Final Project  25%
Class Participation  5%
Incomplete
grades
will
be
given
only
for
verifiable
medical
illness
or
other
such
dire
circumstances.
Here is how the final weighted average will map to final letter
grades:
Rounded Percentage 
Letter grade 
Rounded Percentage  Letter grade  
97% 100% 
A+ (4.0) 
77%  79%  C+ (2.3)  
93%  96%  A (4.0)  73%  76%  C (2.0)  
90%  92%  A (3.7)  70%  72%  C (1.7)  
87%  89%  B+ (3.3)  67%  69%  D+ (1.3)  
83%  86%  B (3.0)  60%  66%  D (1.0)  
80%  82%  B (2.7)  0%  59%  F (0.0) 
Submission and Late Policy
All
work
must be turned in either in hardcopy or electronic submission,
depending on the instructions given in the assignment. Email
submissions, when permitted, should request a "delivery receipt" to
document time and date of submission. Extensions will be given
only in the case of verifiable medical excuses or other such dire
circumstances, if requested in advance.
Late
submissions will receive a penalty of 10% for every 024 hours it is past
the due date and time (e.g., assignments turned in 25 hrs late will
receive a penalty of 20%). Submissions received more than one
week
late will not be accepted.
That
being said, everyone will receive 4 "free" late days that you can
choose to use for any assignment (except the final project). "How
do I choose when to use them?," you ask. You don't  I will add
them in at the end of the semester to maximize your grade. Unused
late days will be worth some unspecified amount of benevolence points.
Exams
There will be two exams in this course. The exams will be
closedbook and closednotes. They
will cover material from lectures, homeworks, and assigned readings
(including topics not discussed in class). So, keep up with those
readings!
Study Groups
I want to encourage you to discuss the material and work together
to
understand it. Here are my thoughts on collaborating with other
students:
If you have any questions as to what types of collaborations are
allowed, please feel free to ask.
Thanks
to Terran Lane, Tom Mitchell, Andrew Moore, and Tim Oates for making
their course materials
available. Many of
the course materials for this class have been adapted from those
sources.