Machine Learning
CS 4780/5780  Fall 2012 

Time and Place  
First lecture: August 23, 2012 Last lecture: November 29, 2012
First Prelim Exam: October 16  
Instructor  
Thorsten Joachims (homepage)  
Online Resources  


Teaching Assistants and Consultants  
Igor Labutov, TA (homepage) Moontae Lee, TA (homepage) Joshua Moore, TA (homepage) Harry Terkelsen, Consultant Declan Boyd, Consultant Jason Zhao, Consultant Joe Mongeluzzi, Consultant Kyle Hsu, Consultant Joseph Staehle, Consultant Emma Kilfoyle, Consultant 

Office Hours  
Time  Person  Location  
Monday, 3:30 to 4:30  Moontae Lee  Upson 328 Bay A  
Monday, 4:30 to 5:30  Harry Terkelsen  Upson 328 Bay A  
Tuesday, 4:00 to 5:00  Declan Boyd  Upson 328 Bay A  
Tuesday, 5:00 to 6:00  Josh Moore  Upson 5162  
Tuesday, 6:00 to 7:00  Igor Labutov  Upson 328 Bay B  
Wednesday, 3:30 to 4:30  Moontae Lee  Upson 328 Bay A  
Wednesday, 5:00 to 6:00  Joey Staehle  Upson 328 Bay A  
Wednesday, 6:00 to 7:00  Igor Labutov  Upson 328 Bay B  
Thursday, 2:40 to 4:00  Thorsten Joachims  Upson 4153  
Friday, 3:00 to 4:00  Josh Moore  Upson 5162  
Friday, 4:00 top 5:00  Harry Terkelsen  Upson 328 Bay A  
Saturday, 4:00 to 5:00  Emma Kilfoyle  Upson 328 Bay A  
Sunday, 4:00 to 5:00  Declan Boyd  Upson 328 Bay A  
Syllabus  
Machine learning is concerned with the
question of how to make computers learn from experience. The ability to
learn is not only central to most aspects of intelligent behavior, but
machine learning techniques have become key components of many software
systems. For examples, machine learning techniques are used to create
spam filters, to analyze customer purchase data, to understand natural
language, or to detect fraudulent
credit card transactions.
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, to clustering and matrix factorization methods for recommendation. 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:


Slides and Handouts  
08/23: Introduction (PDF) 

08/28: InstanceBased Learning (PDF)  
08/30: Decision Tree Learning (PDF)  
09/06: Prediction and Overfitting (PDF)  
09/11: Model Selection and Assessment (PDF)  
09/13: Linear Classifiers and Perceptrons (PDF)  
09/20: Optimal Hyperplanes and Support Vector Machines (PDF)  
09/25: SVM Duality and Leaveoneout Bounds (PDF)  
10/02: Kernels (PDF)  
10/04: Learning to Rank (PDF)  
10/11: Recommender Systems and Matrix Decomposition (PDF)  
10/23: Learning through Generative Modeling (PDF)  
10/30: Sequence Prediction and Hidden Markov Models (PDF)  
11/06: Statistical Learning Theory (PDF)  
11/13: Clustering (PDF)  
Reference Material  
The main textbooks for the class are
An additional textbook that can serve as a brief secondary reference on many topics in this class is
The reading in the course packet are taken from the following books. In addition, these are some books for further reading beyond the scope of the course:


Prerequisites  
Programming skills (e.g. CS 2110 or CS 3110), and basic knowledge of linear algebra (e.g. MATH 2940) and probability theory (e.g. CS 2800).  
Grading  
This is a 4credit course. Grades will be
determined based on two written exams, a final project, homework
assignments, and class participation.
To eliminate outlier grades for homeworks and quizzes, the lowest grade is replaced by the second lowest grade when grades are cumulated at the end of the semester. All assignments are due at the beginning of class on the due date. Assignments turned in late will be charged a 1 percentage point reduction of the cumulated final homework grade for each period of 24 hours for which the assignment is late. However, every student has a budget of 5 late days (i.e. 24 hour periods after the time the assignment was due) throughout the semester for which there is no late penalty. So, if you have perfect scores of 100 on all 5 homeworks and a total of 7 late days, you final homework score will be 98 (which then accounts for 35% of your course grade). No assignment will be accepted after the solution was made public, which is typically 4 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 305 (note the change of room, opening hours Monday  Thursday 12:00pm  4:00pm, Friday: 12:30pm  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=92100; B=8288; C=7278; D=6068; F= below 60 

Academic Integrity  
This course follows the
Cornell University Code of Academic Integrity. Each student in this
course is expected to abide by the Cornell University Code of Academic
Integrity. Any work submitted by a student in this course for academic
credit will be the student's own work. Violations of the rules (e.g.
cheating, copying, nonapproved collaborations) will not be tolerated. We run automatic cheating detection to detect violations of the collaboration rules. 