Bryn Mawr College
CMSC 380: Machine Learning
  Spring 2011

 Syllabus and Schedule
 Course Information

Instructor Information
Textbooks and Software  Course Policies
Additional Resources

Syllabus and Schedule

This is a tentative syllabus and schedule.  Topics, reading assignments, and due dates are subject to change.
All homework assignments are due by the start of class (10:10am) on the day listed.

Wk Date Topic Reading Lab Assignments Comments
Introduction to Machine Learning
Alpaydin Ch. 1
No lab

kNN notes
Statistical Learning, Empirical Methodology, Decision Trees
Alpaydin Ch. 2
Mitchell Ch. 3

Weka tutorial
Assignment 1 out


3 1/31
Review of Linear Algebra, Linear Discriminants Alpaydin 10.1-10.7
Matlab tutorial

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
No Class -- Eric out of town

5 2/14
SVMs & Kernel Methods (continued)
Alpaydin 13.1-13.7
Probability review

Generative Modeling, Probability Review
Alpaydin Appx. A, 3.1-3.5, 4.1-4.4

6 2/21
Bayes Rule, Graphical Models,
Basic Bayesian Nonparametrics
Logistic Regression
Alpaydin 4.5-4.8, 16.1-16.3
Assignment 2 due
Assignment 3 out

Alpaydin 8.1-8.4

7 2/28
Logistic Regression (continued), MLE and MAP
Ensemble Methods
Alpaydin 5.1-5.5
Alpaydin 10.7

Alpaydin 12.8-12.9, 17.1-17.4, 17.6-17.9

8 3/7
No Class -- Spring Break

9 3/14
Exam 1

Assignment 3 due
Exam 1 Equation Sheet
Semi-supervised and Unsupervised Learning Alpaydin 7.1-7.4
Assignment 4 out

10 3/21
Semi-supervised and Unsupervised Learning (continued) Alpaydin 7.5-7.8

Relational Learning Primer

Jen Neville's visit on Wednesday
11 3/28
Semi-supervised learning for text
and image classification

Tutorial on Feature Learning for Images: Part 0, Part 1
Transfer and Lifelong Learning
Assignment 4 due
Project Description

12 4/4
Transfer Learning (continued) /
Dimensionality Reduction
Alpaydin 6.1-6.8

Dimensionality Reduction, Manifold Methods

Dimensionality Reduction Notes
13 4/11
Manifold Methods / Isomap

Reinforcement Learning and MDPs Sutton & Barto
RL Notes
Reinforcement Learning and MDPs Sutton & Barto


Adv. Topics / Review

Exam 2

Project Presentations during Final Exam slot

Final Project Due

General Course Information

Description:  Machine learning has been essential to the success of many recent technologies, including autonomous vehicles, search engines, DNA sequencing, automated medical diagnosis, and facial recognition. This course will cover the fundamental concepts and algorithms that enable computers to learn from experience, and their practical application to real-world problems.  Topics include decision trees, logistic regression, linear methods, support vector machines, kernel methods, genetic algorithms, reinforcement learning, probabilistic Bayesian methods, graphical models, mixture models, ensemble techniques, evaluation methodologies, and emerging developments in transfer and lifelong learning.

CMSC B110, CMSC/MATH B231, or permission of instructor.

Lecture Hours: Mondays, Wednesdays, 10:00-11:30 am
Room: Park 230

Lab Hours: Wednesday 2-4pm
Lab Room: Park 232


Contact Information and Office Hours

Professor: Eric Eaton
Office:  Park 249
Office Phone:  610-526-5061

E-mail is the best way to reach me, and I make a concerted effort to respond to all e-mails within 24 hours on weekdays and 48 hours on weekends (often, much less!).

Office hours: 1-2pm Tuesday/Thursday and by appointment

Textbooks & Software





Recommended Resources:

Course Policies


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 two-way 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 course-related 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 in-class.

Whenever you e-mail me, please use a meaningful subject line and include the phrase "CS380" at the beginning of that line. Your e-mail will catch my attention and I will respond quicker if you do this. I make an effort to respond to e-mails 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 e-mail, phone call or face-to-face 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.


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)

The instructor reserves the right to adjust the percentage ranges for each letter grade upward in your favor.

Submission and Late Policy

All work must be turned in either in hard-copy or electronic submission, depending on the instructions given in the assignment.  E-mail 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 0-24 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.


There will be two exams in this course.  The exams will be closed-book and closed-notes.  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.

Additional Resources

  • LaTeX Resources
  • Tutorials
  • Feature learning for images

  • 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.