CS 8751 Course Syllabus
Machine Learning and Knowledge Discovery in Databases
Spring 2009

Course Information

Instructor:Rich Maclin
Office:315 Heller Hall
Lectures:16:00-17:40 Monday, Wednesday, MWAH 175
Office Hours:14:30-15:30 Tuesday, 14:45-15:45 Wednesday and by appointment
Texts:Mitchell, Machine Learning, McGraw-Hill

Course Coverage

This course will present an introduction to the field of machine learning and the related field of data mining. The course will cover material from both textbooks focusing primarily on Mitchell's book. The textbooks will be supplemented with material from research papers on recent developments in machine learning. The course will focus largely on classification learning, though there will also be some coverage of other learning techniques including analytical learning, unsupervised learning and reinforcement learning. The course will include several coding projects in which students will implement learning algorithms. Each student will also make a presentation and write a summary of an existing piece of research in the field.

Examinations, Assignments and Grades

ItemPointsDate and Time
Midterm Exam 1 150 points February 18 (Wednesday), 16:00-17:40
Midterm Exam 2 150 points April 1 (Wednesday), 16:00-17:40
Final Exam 300 points May 11 (Monday), 16:00-17:55
Homework Assignments (5) 100 points TBA
Programming Assignments (3) 100 points TBA
Research Paper Oral Presentation 50 points TBA
Research Paper Implementation 100 points TBA
Research Paper Web Page 50 points TBA
Total 1000 points Grade based on total points

Grades are assigned on a percentage basis, and then an adjustment is applied based on a minimum effort requirement (see below). The grade percentage cutoffs are as follows:

These percentages may be lowered but will not be raised.

Minimum Effort Requirement: Students must turn in a minimal credible effort for EVERY assignment or their grade will be reduced one full letter grade (an A would become a B, an A- a B-, a B+ a C+, etc.). A turned in assignment achieving at least 40% of the possible points (before late assignment penalties) will be considered a minimal credible effort (though this percentage may be revised downwards by the instructor as warranted). For example, if a program has a maximum possible 30 points, then a turned-in assignment achieving at least 12 points before late penalties would be considered a minimal credible effort.

Research paper presentation: each student will select, with guidance, a recent paper covering a new topic in the field of machine learning, perhaps related to your thesis research. You will make a presentation about this paper (approximately one hour), implement the algorithm in the paper (or some significant part) and create an online presentation of your implementation.


Course Material

Copies of the overheads used in class will be made available on the class web page.

Missed Classes

You are responsible for what goes on in class, including lecture material, handouts, and turning in assignments. If you are unable to attend class it is your responsibility to obtain copies of class notes and any materials distributed in class. You may turn in copies of assignments early or have other members of the class turn in an assignment for you.

Missed Exams

No exam will be given early. Exams can be made up only in the case of emergencies such as severe illness or death in the immediate family. You must contact me 24 hours in advance in order to arrange a makeup.


All assignments will be collected at the beginning of class on the due date. Late assignments will be penalized 20% of the grade for each working day the assignment is late.


Programming assignments must be your own work. You may discuss general ideas with other students, but should not discuss actual code with others. If you are having problems with an assignment, please come and see me or send me email.

Equal Opportunity

As instructor I shall make every attempt to treat all students equally, without regard to race, religion, color, sex, handicap, age, veteran status, or sexual orientation. I encourage you to talk to me about your concerns of equal opportunity in the classroom. To inquire further about the University's policy on equal opportunity, contact the Office of Equal Opportunity (6827), 269-273 DAdB.

Students With Disabilities

If you have any disability (either permanent or temporary) that might affect your ability to perform in this class, please inform me at the start of the semester. I may adapt methods, materials, or testing so that you can participate equitably. To learn about the services that UMD provides to students with disabilities, contact the Access Center (8727), 138 Kirby Plaza, or the Office of Equal Opportunity (8217), 269-273 DAdB.