Empirical Methods in Machine Learning & Data Mining
Computer Science Department
Cornell University
Fall 2004

General Information    Lecture Notes    ML Links    Assignments    Project


Time and Place




Email (@cs.cornell.edu)

Office Hours



Rich Caruana

Tue 4:30 - 5:00
Wed 10:30-11:30

Upson 4157

Teaching Assistant

Cristian Bucila

 Thu 11:30-12:00
Fri 2:30-3:30

Upson 344

Teaching Assistant

Mohamed Elhawary

Mon 3:30 -4:30
Fri 10:00-11:00

Upson 4162

Teaching Assistant

Alex Niculescu-Mizil

Tue 4:30-5:30

Upson 5154

 Administrative Asst.

Cindy Robinson 

M-F 7:30-3:30

Upson 4146

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General Information

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.

Tentative Course Syllabus

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:

Academic integrity policy

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Lecture Note

Decision tree lecture notes: CS578.04_DT_lecture.pdf

KNN/MBL/CBR lecture notes: CS578_knn_lecture.ppt

Missing Values & Feature Selection lecture notes: missing_featsel_lecture.pdf

Performance Measures lecture notes: performance_measures.pdf

Bagging and Boosting lecture notes: CS578.bagging.boosting.lecture.pdf

Multitask learning lecture notes: cs578.mtl.lecture.pdf

Clustering lecture notes: cs578_clustering_lecture.pdf

SVM lecture notes

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Homework 1 decision tree download: cs578.hw1.tar

IND download for MacOS 10.3: ind.macos10.3.tar

UnixStat download for MacOS 10.3: unixstat.macos10.3.tar

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Final Project

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ML Links

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