Instructor:
Stephen ScottCourse home page: http://www.cse.unl.edu/~sscott/CSCE478
Ferguson 305
sscott AT cse
http://www.cse.unl.edu/~sscott/
Fall 2003 offering: http://www.cse.unl.edu/~sscott/CSCE478-fall03
Fall 2001 offering: http://www.cse.unl.edu/~sscott/CSCE478-fall01
Fall 1999 offering: http://www.cse.unl.edu/~sscott/CSCE496-ML
Meeting Time: Monday/Wednesday/Friday 12:30-1:20, room TBA
(This is a tentative meeting time. If agreed upon by all
registered students, the time may be changed.)
Track Classification: This course fulfills Applications track requirements for all undergraduate and graduate programs in Computer Science and Computer Engineering. It also meets an Integrative Studies (IS) requirement for the BS degree.
Credits: 3 units
Prerequisites: The only prerequisite is CSCE 310. STAT 380 is useful, but not required (we will review all necessary material from probability & statistics).
Textbook: Machine Learning by Tom M. Mitchell, McGraw-Hill, 1997
Course Description:
Building machines that learn from experience is an important research goal of artificial intelligence (AI). The field of machine learning is a subarea of AI that is concerned with the question of how to construct computer programs that automatically improve with experience. In recent years many successful machine learning applications have been developed, including data mining programs that learn to detect fraudulent credit card transactions, information-filtering systems that learn users' reading preferences, face-recognition systems that learn to automatically identify people in images, biological sequence analysis programs that learn how to search massive databases for proteins belonging to certain families, and autonomous vehicles that learn to drive on public highways. At the same time, there have been important advances in the theory and algorithms that form the foundations of this field.
The goal of this course is to present the key algorithms and theory that form the core of machine learning. Machine learning draws on concepts and results from many fields, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity, algorithm design and analysis, and control theory. In this course we will review the field of machine learning in these contexts and work to understand the problem settings, algorithms, and assumptions that underlie each.
The topics the course will cover include decision trees, neural networks, support vector machines, hypothesis testing, Bayesian learning, computational learning theory, instance-based learning, genetic algorithms, reinforcement learning, and learning sets of rules. This course differs from the courses on neural networks, genetic algorithms, and artificial intelligence currently offered in that it gives a broad overview of machine learning rather than going deep into a single topic. At the end of the course, the students will sufficiently understand the fundamentals of these areas of machine learning to begin basic research in the area and to understand the ideas of technical papers in machine learning.
Grades in this course will be based on homework exercises, written topic summaries, and a project. For information on previous offerings of this course (fall 1999, fall 2001, and fall 2003), see http://www.cse.unl.edu/~sscott/CSCE496-ML, http://www.cse.unl.edu/~sscott/CSCE478-fall01 and http://www.cse.unl.edu/~sscott/CSCE478-fall03
Last modified 16 August 2011; please report problems to sscott AT cse.