Empirical Methods in Machine Learning & Data Mining
CS578
Computer Science Department
Cornell University
Fall 2004

General Information    Lecture Notes    ML Links    Assignments    Project

Announcements:

• Paper on MultiTask Learning: mlj97.ps
• Important: students who complete a 578 course evaluation receive 5 extra grade points.
• Final project prediction submissions: 
•     to register:  http://kodiak.cs.cornell.edu/kddcup/cs578/register.html
•     to submit predictions: http://kodiak.cs.cornell.edu/kddcup/cs578/submission.html
• Final Exam is Fri, Dec 10th, 3:00 -  5:30 pm in Upson B17.  Open book and Open notes.
• Final report officially is due Sat Dec 4.  Reports will not be accepted after 12 noon Wed Dec 8.
• Final Project now out.  Predictions due midnight Sat Dec 4.  project.tar.gz
• Here's the PERF software for HW3 and the Final Project:  http://kodiak.cs.cornell.edu/kddcup/software.html
• HW3 now out:  Due 2:55pm Thu Nov 18.  No extensions!  hw3.ps
• Take-home prelim is due 2:55pm Tue, Nov 2.  Here's the text: trick-or-treat
• HW2 extension: HW2 is due 2:55pm Thu Oct 21.
• HW2 now out.  Due 2:55pm Tue Oct 19.  hw2.data.gz   hw2.ps
• HW1 Extension: We are extending the deadline for Homework 1 to 2:55pm Thu, Sep 23.
• Linux/Unix/Scripting/UnixStat tutorial session Tue 7:00pm-8:30pm in Thurston 204
• For those using MacOS X, we have downloads for IND and UnixStat that compile on Mac.
• Homework 1 now available for download: cs578.hw1.tar

Time and Place

Personnel

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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

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:

• 20% Midterm (take home)
• 20% Final (open book in class)
• 30% Assignments (individual)
• 30% Final Project (group project comparing various learning methods on two test problems)
• Bonus points for class participation
• 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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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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Assignments

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

• Final project prediction submissions: 
•     to register:  http://kodiak.cs.cornell.edu/kddcup/cs578/register.html
•     to submit predictions: http://kodiak.cs.cornell.edu/kddcup/cs578/submission.html
• Final report officially is due Sat Dec 4.  Reports will not be accepted after 12 noon Wed Dec 8.
• Final Project now out.  Predictions due midnight Sat Dec 4.  project.tar.gz
• Here's the PERF software for HW3 and the Final Project:  http://kodiak.cs.cornell.edu/kddcup/software.html

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ML Links

• The UC-Irvine ML Dataset Archive | The UC-Irvine KDD archive | more datasets
• ML Projects
  • The WEKA Machine Learning Project (code for many ML algo's, as well as some datasets)
• Journals
  • Machine Learning | Journal of ML Research | Data Mining and Knowledge Discovery | Journal of AI Research | AI Magazine | IEEE Neural Networks Council (several journals are connected to this page)
• Knowledge Discovery in Databases

• Other University ML classes
  • Wisc Madison More External AI References
• Pointers to ML Courses
• Machine Learning Benchmarking
• AI Bibliography Server | Neural Networks Bibliography Server (Austrian AI Institute)
• AI Resources (Canadian NRC Server)
• Aha's ML Links
• Stuart Russell's:AI on the Web (loads of links)
• Reinforcement Learning Repository

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