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CS 480/697 Applied Machine Learning
Course Syllabus
Spring 2013

Instructor: Dr. Wei Ding
Office: S-3-167, Science Building , 3rd floor
Class Schedule:TTH 4:00 - 5:15 PM,  Science Building the 3rd floor S-3028
Pre-requisites: Math129 or Math130 or appropriate scores on the Math placement exam
Office Hours: Monday 6:00 PM - 7:00 PM
Wednesday 6:00 PM - 7:00 PM
TA: Mr. Yang Mu
E-mail: yangmu AT
Office Hours: KDLab Lab S-3-158A
Schedule an appointment with your TA by email.


Data Mining: Practical Machine Learning Tools and Techniques
Ian H. Witten, Eibe Frank, Mark A. Hall
ISBN-10: 0321498054 | ISBN-13: 978-0321498052
Morgan Kaufmann
ISBN 978-0-12-374856-0
Lecture Notes will be posted at UMass Online and various online websites recommended by the Instructor.


  1. To learn practical side of machine learning for applications
  2. To learn the process of applying meachine learning to a variety of problems
  3. To learn classification
  4. To learn clustering
  5. To learn association rules


Lecture and interactive problem solving.


Participation: 5% of the total
Programming Assignments: 60% of the total
Examination (2 exams): 35% of the total


91+ = A; 89+ = A-;
87+ = B+; 83+ = B; 80+ = B-;
77+ = C+; 73+ = C; 70+ = C-;
67+ = D+; 63+ = D; 60+ = D-;
0+ = F;


We will read from the recommended text book, various sources on the web, and slides that will be made available on the web site. The schedule for the readings are given on the schedule web page.


  1. Homework:
  2. Providing answers for any examination when not specifically authorized by the instructor to do so, or, informing any person or persons of the contents of any examination prior to the time the examination is given is considered cheating.
  3. Penalty for cheating will be extremely severe. Use your best judgment. If you are not sure about certain activities, consult the instructor. Standard academic honesty procedure will be followed for cheating and active cheating automatically results F in the final grade. Please check University Policy on Academic Standards and Cheating for additional information.
  4. You are expected to come fully prepared to every class.
  5. No incomplete grade under nearly all situations.
  6. There is no formal attendance policy. However, you are responsible for everything discussed in class. You may receive a zero for lack of participation.
  7. Pay very careful attention to your email correspondence. It reflects on your communication skills. Avoid using non-standard English such as "how r u?" in your email message. In addition, I recommend you put the class number 480/697 and a brief summary of your question in your email subject. For example,

    Subject: CS 480/697 A question on clustering

  8. I immediately discard anonymous emails.
  9. The ringing, beeping, buzzing of cell phones, watches, and/or pagers during class time is extremely rude and disruptive to your fellow students and to the class flow. Please turn off all cell phones, watches, and pagers prior to the start of class.

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