CS 4780/5780 - Fall 2011
Cornell University
Department of Computer Science

• Tuesday, 1:25pm - 2:40pm in Hollister B14
• Thursday, 1:25pm - 2:40pm in Hollister B14

First Prelim Exam: October 13
Second Prelim Exam: November 22
Project Due Date: December 15

• [Piazza Forum]  We greatly prefer that you use this Piazza forum for questions and discussions.  The forum is monitored all the TA's and the prof -- you will get the best response time. And all the TA's will know the question you asked and the answers you receive.
• [CMS] Assignments, the prereq exam, and all grades are posted on CMS.
• [Videonote] Recordings of the lectures with annotations are available through Videonote.

This course will introduce the fundamental set of techniques and algorithms that constitute machine learning as of today, ranging from classification methods like decision trees and support vector machines, over structured models like hidden Markov models and context-free grammars, to unsupervised learning and clustering. The course will not only discuss individual algorithms and methods, but also tie principles and approaches together from a theoretical perspective. In particular, the course will cover the following topics: 

• Concept Learning : Hypothesis space, version space
• Instance-based Learning : K-Nearest Neighbors, collaborative filtering
• Decision Trees : TDIDT, attribute selection, pruning and overfitting
• ML Experimentation : Hypothesis tests, resampling estimates
• Linear Rules : Perceptron, duality, mistake bound
• Support Vector Machines : Optimal hyperplane, kernels, stability
• Generative Models : Naïve Bayes, linear discriminant analysis
• Hidden Markov Models: probabilistic model, estimation, Viterbi
• Structured Output Prediction : predicting sequences, rankings, etc.
• Learning Theory : PAC learning, mistake bounds, VC dimension
• Clustering : HAC, k-means, mixture of Gaussians

• Tom Mitchell, "Machine Learning", McGraw Hill, 1997.
• Cristianini, Shawe-Taylor, "Introduction to Support Vector Machines", Cambridge University Press, 2000. (online via Cornell Library)
• Schoelkopf, Smola, "Learning with Kernels", MIT Press, 2001. (online)

An additional textbook that can serve as a brief secondary reference on many topics in this class is

Ethem Alpaydin, "Introduction to Machine Learning", MIT Press, 2004.

In addition, there will be additional readings for topics not covered in the main textbooks. For further reading beyond the scope of the course, we recommended the following books:

• Bishop, "Pattern Recognition and Machine Learning", Springer, 2006.
• Devroye, Gyoerfi, Lugosi, "A Probabilistic Theory of Pattern Recognition", Springer, 1997.
• Duda, Hart, Stork, "Pattern Classification", Wiley, 2000.
• Hastie, Tibshirani, Friedman, "The Elements of Statistical Learning", Springer, 2001.
• Joachims, "Learning to Classify Text using Support Vector Machines", Kluwer, 2002.
• Leeds Tutorial on HMMs (online)
• Manning, Schuetze, "Foundations of Statistical Natural Language Processing", MIT Press, 1999. (online via Cornell Library)
• Manning, Raghavan, Schuetze, "Introduction to Information Retrieval", Cambridge, 2008. (online)
• Vapnik, "Statistical Learning Theory", Wiley, 1998.

• 40%: 2 Prelim Exams
• 15%: Final Project
• 35%: Homework (~5 assignments)
• 5%: Quizzes (in class)
• 2%: Prereq Exam
• 3%: Class Participation

To eliminate outlier grades for homeworks and quizzes, the lowest grade is replaced by the second lowest grade in the final grade computation.

All assignments are due at the beginning of class on the due date. Assignments turned in late will be charged a late penalty of 5 points for each period of 24 hours for which the assignment is late. However, every student has a budget of 4 late days (i.e. 24 hour periods after the time the assignment was due) throughout the semester for which there is no late penalty. No assignment will be accepted after the solution was made public, which is typically 5 days after the time it was due. You can submit late assignments in class, in office hours, or to the office of a TA.

Graded homework assignments and prelims can be picked up in Upson 360 (opening hours Monday - Thursday 12noon - 4:00pm, Friday: 1:30pm - 4:00pm). Regrade requests can be submitted within 7 days after the grades have been made available on CMS. Regrade requests have to be submitted in writing and in hardcopy using this form (or similar). They can be submitted in class, in office hours, or to the office of a TA.

We always appreciate interesting homework solutions that go beyond the minimum. To reward homework solutions that are particularly nice, we will give you "Bonus Points". Bonus points are collected in a special category on CMS. Bonus points are not real points and are not summed up for the final grade, but they can nudge somebody to a higher grade who is right on the boundary.

All assignment, exam, and final grades are roughly on the following scale: A=92-100; B=82-88; C=72-78; D=60-68; F= below 60

We run automatic cheating detection to detect violations of the collaboration rules.