Course Announcement for Fall 1999
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****** CSCE 496/896: Special Topics---Machine Learning ******
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Instructor: Stephen Scott
Ferguson 305
472-6994
sscott@cse.unl.edu
http://www.cse.unl.edu/~sscott/
Meeting Time: Monday/Wednesday/Friday 11:30--12:20, Ferguson 112
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, ranging from data-mining programs that learn to detect
fraudulent credit card transactions, to information-filtering systems
that learn users' reading preferences, to 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, 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, exams, and
a project.