Last updated on 20120725 at 09:58.
CS 4641 is an introductory survey of modern machine learning. Machine learning is an active and growing field that would require many courses to cover completely. This course aims at the middle of the theoretical versus practical spectrum. We will learn the concepts behind several machine learning algorithms wtihout going deeply into the mathematics and gain practical experience applying them. We will consider pattern recognition and artificial intelligence perspectives, making the course valuable to students interested in data science, engineering, and intelligent agent applications.
Required:
Ethem Alpaydin, Introduction to Machine Learning, Second Edition http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12012
This book will cover all the material in the course.
Recommended:
Stephen Marsland, Machine Learning: An Algorithmic Perspective http://www.amazon.com/MachineLearningAlgorithmicPerspectiveRecognition/dp/1420067184
Haven't read this one thoroughly, but it seems to be at the same level as Alpaydin's book and has extensive Python code (using NumPy, like you would in practice) at http://wwwist.massey.ac.nz/smarsland/MLBook.html
Christopher M. Bishop, Pattern Recognition and Machine Learning http://research.microsoft.com/enus/um/people/cmbishop/prml/
Excellent book with deeper coverage of mathematics than Alpaydin. You'll read this if you continue studying machine learning.
Tom Mitchell, Machine Learning http://www.cs.cmu.edu/~tom/mlbook.html
A great book at the same level as Alpaydin's. If you ever need another perspective and want a great explanation, check out this book. It's only drawback as a course text is that it's 15 years old and doesn't cover many of the course topics (like PCA and SVMs).
Homework helps you learn the concepts we discuss in class and read about in our book. Exams test your understanding of these concepts. I will assign homework every day to help you prepare for the exams. They will not be graded, but we will review the answers to help you assess your own understanding.
Projects give you practical experience applying machine learning algorithms to realworld data. Each project is designed to highlight specific conceptual and practical issues to develop your intuition about how to use machine learning for your own problems.
Conceptual understanding and practical application will be graded equally:
Final letter grades will be determined by clustering the distribution of course averages. This clustering will be as generous as possible consistent with applicable policy. Guaranteed grades will be determined by:
val letterGrade = courseAverage match { case avg if avg >= 90 => 'A case avg if avg >= 80 => 'B case avg if avg >= 70 => 'C case avg if avg >= 60 => 'D case _ => 'F }
Note that in the past, scores have been much lower than the guaranteed scores listed above. For example, on the last midterm exam a score of 30 was a B. It's the distribution that matters, not the raw score.
Class resources, including all assignments, will be maintained on this openaccess web site. Projects will be submitted and grades will be maintained in TSquare.
Date  Topics  Assignments 

20120514  Introduction  Reading: I2ML 1 HW: I2ML 1.1,6,7,8,9 
20120516  Supervised Learning  Reading: I2ML 2 HW: I2ML 2.1, 2, 4, 5, 7, 8 
20120521  Reading: I2ML 3.13.5, 8.18.5 HW: I2ML 3.1, 2, 5; 8.2, 3, 4 

20120523  Reading: I2ML 9, 19.1, 19.519.7 HW: I2ML 9.1, 2, 4 Assignment: Project1 

20120528  Memorial Day Holiday  
20120530  Reading: I2ML 10, 11.111.8.2 HW: 10.1,79 ; 11.13 

20120604  Online Review  Work on Project 1 
20120606  Online Office Hours  Work on Project 1 
20120611  Exam 1  Reading: I2ML 14, 811 Due 20120611: Project1 
20120613  Exam 1 and Project 1 Review  Withdrawal deadline: 20120615 
20120618  Parametric & Multivariate  Reading: I2ML 4, 5 
20120620  Dimensionality Reduction  Reading: I2ML 6, PCA Primer 
20120625  Clustering  Reading: I2ML 7 
20120627  Kernel Machines  Reading: I2ML 13 Assignment: Project2 
20120702  Combining Learners  Reading: I2ML 17 
20120704  Independence Day Holiday  
20120706  Exam 2 Review Session  15:00 in CoC 101 
20120709  Exam 2  Reading: I2ML 47, 1213, 17 
20120716  Reinforcement Learning  Reading: I2ML 18.118.4 Due 20120716: Project2 
20120718  Reinforcement Learning  Reading: I2ML 18 all Assignment: Project3 
20120723  Hidden Markov Models  Reading: I2ML 15 
20120725  Course Review  Reading: I2ML Due 20120730: Project3 
20120801 8:0010:50 
Final Exam  Reading: I2ML 1516, 18 