General
Information Lecture Notes
ML Links
Assignments
Project
Announcements:
- Predictions for the project are due Wed
night at 11:59pm. Reports are due Friday at noon. Good luck on the
project!
- The review session will be Mon Dec 10
7pm-9pm in Hollister 206 (same room as classes). This session is
completely optional. The session will be question driven, some bring
good questions that you would like answered.
- To create a group for the final project go
to: http://kodiak.cs.cornell.edu/kddcup/cs578/register.html
- To submit predictions for the final project
go to: http://kodiak.cs.cornell.edu/kddcup/cs578/submission.html
- To view results on the 1st 100 test cases
go to: http://kodiak.cs.cornell.edu/cgi-bin/cs578/newtable.pl
- Data for final project is now available: cs578.final.project.
Predictions are due Wed Dec 12 at noon and reports are due Thu Dec 13 at
noon.
- HW3 is now available. It is due at the start of class on Tue Nov
27.
- The take-home midterm exam will be handed out Tue
Oct 30, and is due back start of class Tue Nov 6.
trick-or-treat.txt trick-or-treat.ps
trick-or-treat.pdf
- The Final Exam for 578 is Tue, Dec 11 9:00 - 11:30 am. The final
exam is open books, open notes (no computers or wireless devices).
- HW2 is now
due Tue Oct 23 at the beginning of class (instead of Thu Oct 18).
- Assignment 2 now available. Due start of class
on Tue October 18. Have fun!
- You have two extra days to do HW1. HW1 is now
due Thu Sep 20 at the beginning of class (instead of Tue Sep 18).
- Note about Homework 1: Prediction column is the last column "amegfi" in the dataset, which takes 2 values.
- Tue September 4, 7:30-9:00pm, Hollister 206 (same room as the class): Shell scripting and UNIX tutorial session.
- Assignment 1 now available. Due BEGINNING OF CLASS on Tue September
18. Have fun!
-
Welcome to 578!!!
Time
and Place:
2:55 PM to 4:10 PM
Tuesdays & Thursdays
206 Hollister
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Email (@cs.cornell.edu)
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Office
Hours
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Office
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Instructor
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Rich Caruana
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caruana
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Tue 4:30 - 5:00
Wed 10:30-11:30
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Upson 4157
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Teaching Assistant
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Daria Sorokina
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daria |
Mon 11:00-12:00
Thu 13:15-14:15
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Upson 5156
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Teaching Assistant
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Ainur Yessenalina
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ainur |
Mon 2:30-3:30
Wed 2:30-3:30
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Upson 328
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Teaching Assistant
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Alex
Niculescu-Mizil
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alexn
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Fri 10:30-11:30
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Upson 5154
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Administrative
Assistant
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Melissa Totman
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mtotman
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M-F 9:00-4:00
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Upson 4147
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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
Optional references:
The Elements of
Statistical Learning: Data Mining, Inference, and Prediction by T. Hastie, R. Tibshirani,
J. Friedman.
Pattern Classification 2nd edition
by Richard Duda, Peter Hart, & David Stork
Pattern Recognition and Machine Learning by Christopher Bishop
Grading policies:
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25%
Midterm (take home)
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25%
Final (open book in class)
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30%
Assignments (individual)
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20%
Final Project (group project comparing various learning methods)
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Bonus
points for class participation
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Homeworks,
the take-home mid-term, and the final exam must be your own work. For
homework, it is OK to talk with other students about the assignment,
ask each other questions, and in general learn from each other. But the
homework you hand in must be your own work. If other students gave you
significant help with your homework you should briefly acknowledge them
in what you hand in.
Academic
integrity policy
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Introduction to Statistics, Machine
Learning, and Data Mining
Introduction to Decision Trees
Introduction to Hypothesis Testing
Introduction to Machine Learning
Performance Measures
Introduction to Memory Based Learning (KNN, ...)
Missing Values and Feature Selection
SVM lecture (long notes) (short notes you are responsible for)
Bagging and Boosting
Clustering
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Note about Homework 1: Prediction column is the last column "amegfi" in the dataset, which takes 2 values.
Cygwin Installation Tips
IND decision tree code for MacOS ind.macos10.3.tar
UNIXSTAT utility code for MacOS unixstat.macos10.3.tar
Download HW1 here: cs578.hw1.tar
Perf code for calculating ROC performances: http://kodiak.cs.cornell.edu/kddcup/software.html
Download HW2 here: HW2.tar
Download HW3 here: HW3.578.2007.tar
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Predictions for the final project must be submitted by
noon on Wed December 12.
The report for the final project must be
handed in by noon on Thu December 13.
Train
and test sets for final project are available at the top of this web page, or here.
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