Syllabus for Spring 2012
Topics and Reading Assignments
Textbooks
Witten, Frank, and Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2011, 3rd Ed.
Grading
Weekly Assignments  45% 
Exams (2)  30% 
Final Exam  20% 
Class Participation  5% 
This course covers the analysis and visualization of scientific data. Topics will include data management, basic statistical analysis, data mining techniques, and the fundamental concepts of machine learning. Students will also learn how to visualize data using 2D and 3D graphics, focusing on techniques that highlight patterns and relationships. Course projects will use data from active research projects at Colby.
Late Policy:
The weekly assignments will build upon each other, and each week a solution for the prior week's assignment will be posted so that everyone begins each week with a working code base. Assignments turned in after the solutions have been posted will receive no credit. It is better to hand in a partially working assignment than nothing at all.
Daily Topics and Readings
Week  Topics  Reading 

1: 

Tkinter tutorials 
2: 

Numpy tutorials 
3: 

Lecture notes 
4: 

Lecture notes 
5: 

Handouts, Witten and Frank, chapter 7 
6: 

Handouts 
7: 

Witten and Frank, chapters 13 
Spring Break  
8: 

Witten and Frank, chapter 4 
9: 

Witten and Frank, chapters 4, 6 
10: 

Handouts 
11: 

Witten and Frank, chapters 4, 6 
12: 

Witten and Frank, chapter 8, handouts 
13: 

Handouts 