CS 377: Machine Learning and Data Mining
Syllabus
Instructor Information
Textbook
- Data Mining Introductory and Advanced Topics, Margaret H. Dunham,
Prentice-Hall, 2003.
Important Dates
- Take home exam 1: Assigned Monday, 4/25. Due Friday, 4/29 in class.
- Take home exam 2: Assigned Friday, 5/27. Due Wednesday, 6/1 in
class.
- Final project due: Monday, 6/6, end of last exam.
Your Grade
- Assignments: 40%
- Take home exam 1: 20%
- Take home exam 2: 20%
- Class project: 20%
Collaboration
You are encouraged to work together, given the following ground rules:
- Non-computer assignments: You should turn in your own assignment.
You may work with other people, but each of you should be turning in
your own.
- Computer assignments: You may work together on these in pairs, if
you wish. Include everyone's names in documentation at the top. Make
sure to cite any ideas you get from other people, websites, books,
papers, or any other references.
- Take-home exams: Do these completely on your own. You can discuss
them only with me.
- Final project: You may do this in pairs, if you wish.
Programming Environment
You may use any programming language that you wish, so long as it is
supported on our departmental machines and you provide me with ample
instructions on how to compile, run, and test your code.
Homework Policy
- Each assignment will have a specific time for which it will be
due. An assignment turned in late within one day of the due time will be
docked 25%. A program turned in later than one day of the due date but
within two days will be docked 50%. An assignment turned in any time
after this until the last day of classes will be docked 75%. This same
policy applies to take-home exams.
- College policy dictates that there can be no grace period on the
final project.
Details
We will cover the following topics:
- Introduction (Dunham chapters 1-3)
- Classification and Regression Techniques (Dunham chapter 4 +
supplemental readings)
- Clustering (Dunham chapter 4 + supplemental readings)
- Association Rules (Dunham chapter 5 + supplemental readings)
- Web Mining (Dunham chapter 7 + supplemental readings)
- Spatial Mining (Dunham chapter 8 + supplemental readings)
- Temporal Mining (Dunham chapter 9 + supplemental readings)
- Collaborative Filtering (supplemental readings)
- Text Mining (supplemental readings)