Machine Learning
CS 4780 - Fall 2009 |
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Time and Place | |||
First lecture: August 27, 2009 Last lecture: December 3, 2009
First Prelim Exam: 10/15 Review Session I: Wednesday 10/14, 10:00am -
11:00am, in Upson 315 |
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Instructor | |||
Thorsten Joachims, tj@cs.cornell.edu, 4153 Upson Hall. | |||
Mailing List and Newsgroup | |||
[cs4780-l@cornell.edu] We'd like you to contact us by using this mailing list. The list is set to mail all the TA's and profs -- you will get the best response time by using this facility, and all the TA's will know the question you asked and the answers you receive. | |||
Teaching Assistants | |||
Mark Verheggen, mark@cs.cornell.edu, Upson 4161. | |||
Office Hours | |||
Monday, 1:00 pm - 2:00 pm | Mark Verheggen | Upson 328B | |
Thursday, 3:00 pm - 4:00 pm | Thorsten Joachims | 4153 Upson | |
Thursday, 12:15 pm - 1:15 pm | Mark Verheggen | Upson 328B | |
Friday, 2:30 pm - 3:30 pm | Rick Ducott | Upson 328B | |
Syllabus | |||
Machine learning is concerned with the
question of how to make computers learn from experience. The ability to
learn is not only central to most aspects of intelligent behavior, but
machine learning techniques have become key components of many software
systems. For examples, machine learning techniques are used to create
spam filters, to analyze customer purchase data, or to detect fraudulent
credit card transactions.
This course will introduce the fundamental set of techniques and algorithms that constitute machine learning as of today, ranging from classification methods like decision trees and support vector machines, over structured models like hidden Markov models and context-free grammars, to unsupervised learning and clustering. The course will not only discuss individual algorithms and methods, but also tie principles and approaches together from a theoretical perspective. In particular, the course will cover the following topics:
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Slides and Handouts | |||
08/27: Introduction (PDF) 09/01: Instance-based Learning (PDF) 09/03: Decision Tree Learning (PDF) 09/15: Assessing Learning Results (PDF) 09/22: Linear Rules and Perceptron (PDF) 09/29: Optimal Hyperplanes and Support Vector Machines (PDF) 10/01: Duality and Leave-one-out (PDF) 10/06: Kernels (PDF) 10/08: Learning Ranking Functions (PDF) 10/20: Generative Models (PDF) 10/29: HMMs and Structured Output Prediction (PDF) 11/10: Statistical Learning Theory (PDF) 11/17: Clustering (PDF) 11/19: Transduction and Co-Training (PDF) |
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Reference Material | |||
The main textbooks for the class are
A good additional textbook as a secondary reference is
In addition, there will be additional readings for topics not covered in the main textbooks. For further reading beyond the scope of the course, we recommended the following books:
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Prerequisites | |||
Programming skills (e.g. COM S 211 or COM S 312), and basic knowledge of linear algebra and probability theory (e.g. COM S 280). | |||
Grading | |||
This is a 4-credit course. Grades will be
determined based on two written exams, a final project, homework
assignments, and class participation.
All assignments are due at the beginning of class on the due date. Assignments turned in late will drop 5 points for each period of 24 hours for which the assignment is late. In addition, no assignments will be accepted after the solutions have been made available. Roughly: A=92-100; B=82-88; C=72-78; D=60-68; F= below 60 |
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Academic Integrity | |||
This course follows the Cornell University Code of Academic Integrity. Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit will be the student's own work. Violations of the rules (e.g. cheating, copying, non-approved collaborations) will not be tolerated. |