- Instructor: Pedro Domingos (pedrod at cs)
- Office hours: Wed 3:30-4:20
- Office: Allen Center 648
- TA: Hao Lü (hlv at cs)
- Office hours: Thu 2:30-3:20
- Office: Allen Center 216
Schedule
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Topics & Lecture Notes
Readings
1
Mitchell, Ch. 1; Duda, Ch. 1
2
Mitchell, Ch. 3; Duda, Ch. 8
3
Mitchell, Ch. 10; Duda, Ch. 8
4
Instance-based learning (
pdf ppt)
Mitchell, Ch. 8; Duda, Ch. 4
Feb
5
Mitchell, Ch. 6; Duda, Ch. 2 & 3
6
Mitchell, Ch. 4; Duda, Ch. 6
8
Mitchell, Ch. 7; Duda, Ch. 9
Mar
9
Support vector machines (
pdf ppt)
Duda, Ch. 5
10
Clustering and dimensionality reduction (
pdf ppt)
Duda, Ch. 10
Textbooks
Assignments
There will be four assignments, each worth 16% of the final grade, and a final exam worth 36% of the grade.
- Assignment 1, Decision-Tree Learning for Detecting Promoters, due Thu, Jan 21.
- Assignment 2, Rule Induction and Instance-based Learning, due Thu, Feb 4.
- Assignment 3, Bayesian Learning and Neural Networks, due Thu, Feb 18.
- Assignment 4, Model Ensembles, Learning Theory, and SVM, due Thu, Mar 4.
Final
Final was sent on 10:30am Fri, Mar 12 through class mailing
list. It is due on Tue, Mar 16 at 12 noon. If you did not get it,
email TA as soon as possible.
Course Administration and Policies
- Subscribe to the course mailing list.
- Assignments will be done individually unless otherwise specified. You may discuss the subject matter with other students in the class, but all final answers must be your own work. You are expected to maintain the utmost level of academic integrity in the course.
- Assignments may be handed in up to a week late, at a penalty of 10% of the maximum grade per day.
- Comments can be sent to the instructor or TA using this anonymous feedback form.
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