General
Information Lecture Notes
ML Links Assignments
Project
Announcements
Dec 5 Last handout: Notes about exam, final project, and optional 4th hw
Dec 3 Optional make-up homework assignment available: hw4.txt, hw4.data
Nov 15 Final project (due Dec 7) files available: project.txt, train, test, roceasy.c
Nov 7 Homework assignment 3 (postscript, text) available. Due Thu, Nov 21, 2002
Oct 31 Take home mid term exam now available. Due 2:55pm Thu Nov 7, 2002
Oct 10 Homework assignment 2 is available; kNN lecture slides are available
Sept 12 Homework assignment 1 is available.
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Office Hours |
Office |
Instructor |
Richard Caruana |
Tue 4:30-5:00
Wed 1:30-2:30 |
Upson 4157 |
Teaching Assistant |
Alexandru Niculescu-Mizil |
Mon 1:30-2:30
Thu 12:00-1:00 |
Rhodes 419 |
Course Description:
This implementation-oriented course presents a broad introduction to current algorithms
and approaches in machine learning, knowledge discovery, and data mining and
their application to real-world learning and decision-making tasks. The course
also will cover empirical methods for comparing learning algorithms, for
understanding and explaining their differences, for exploring the
conditions under which each is most appropriate, and for figuring out how to get the best possible performance out of them on real problems.
Textbooks:
Machine
Learning by Tom Mitchell
The Elements of Statistical Learning:
Data Mining, Inference, and Prediction by T. Hastie, R. Tibshirani, J. Friedman.
Optional references:
Pattern Classification 2nd
edition by Richard Duda, Peter Hart, & David Stork
Grading
policies:
Assignment
1: Due Thursday September 26
Download IND package
Instructions for installing the IND package
using CYGWIN under Windows
If you have trouble installing the IND
package on Sun try this
Assignment 2:
Due Tuesday
October 29
hw2
handout (same as handout from class)
Download the
dataset: hw2.knn.data
Nov 15 Final project (due Dec 7) files: project.txt,
train, test, roceasy.c