Eric Siegel now provides predictive analytics services at Prediction Impact, Inc., where predictive modeling and data mining are applied to gain customer intelligence.
Welcome to Machine Learning. This course covers technology that allows computers to learn from example, i.e., supervised learning.
In one view of machine learning, the computer searches for a "meta-solution" to a problem, instead of just a solution. That is, instead of solving one instance of the problem, it derives an hypothesis that describes how to solve the problem in general. Think of it this way: you can search for the right words to express a thought, or you can search for the best writing style. Likewise, instead of searching for the best place to drop your next piece when playing the game Tetris, search for an algorithm to do so more efficiently.
In this course, you will:
Learn This! -- a rap about machine learning.
Homework Assignments: (subject to modification)
Select visuals from lectures: (subject to modification)
Text: Machine Learning, by Tom M. Mitchell, available at Papyrus Books, west side of Broadway a couple blocks down from 116th.
Grading : 65% homeworks, 15% midterm, 20% final. There will be about six homework assignments, four involving programming projects and all involving learning theory.
Late homework: Late assignments will be penalized. Note that partial credit will be considered for all incomplete work.
Collaboration: Discussion of material covered in class is strongly encouraged. Some assignments will be designated as collaborative. Otherwise, the work you submit must be your own work. There is a line between discussion and cheating and this line will be strictly enforced.
Open Door Policy: We would like the course to run smoothly and enjoyably. Feel free to let us know what you find good and interesting about the course. Let us know sooner about the reverse. See us, leave us a note, or send us an e-mail.
email: evs at cs dot columbia dot edu