Meeting Times: |
Lectures: MoWeFr 2:00PM -
2:50PM Heb 204W No classes on November 11 (Veterans Day) and November
24-25 (Thanksgiving) |
Instructor: |
|
Office: |
HB 214-B |
Phone: |
963 – 1438 |
E-mail: |
borisk <at>cwu<dot>edu |
Class Web Page: |
|
TA: |
Wendy Stockwell StockweW < at> cwu <dot> edu |
Office hours: |
4:00 - 5: 00 PM M.,F. |
Supplemental text: |
Data
Mining in Finance: Advances in Relational and Hybrid Methods, by B.
Kovalerchuk, |
Grading |
|
Midterm Exam |
15 |
Class Project (serves as Final Exam) |
30 |
Class participation and quizzes (on random days) |
5 |
Lecture notes |
10 |
Labs
|
40 |
Grading Scale |
|
95 – 100 |
A |
90 – 94 |
A - |
87 – 89 |
B + |
83 – 86 |
B |
80 – 82 |
B - |
77 – 79 |
C + |
73 – 76 |
C |
70 – 72 |
C - |
67 – 69 |
D+ |
63-66 |
D |
60 – 62 |
D- |
Grading Note: The projects are an important
part of the course.
Objectives and student learning outcome: This course introduces data mining concepts. Students will learn the basics of data mining algorithm development with an emphasis on real world applications. Students will learn about data types and major algorithmic approaches: Regression, Neural Networks, Decision Trees, DNF rules, relational and hybrid methods, and others.
Honor Code: All work turned in for credit,
including exams and all components of the project, are to be the work of the
student whose name is on the exam or project. For all project components, the
student can receive assistance from individuals other than the instructor only
to ascertain the cause of errors. Thus you can get help if you need it
to figure out why something doesn't work. You just can't get help from anyone,
other than the instructor, to figure out how to make something work. All
solutions turned in for credit are to be your individual work and should
demonstrate your problem solving skills, not someone else's.