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\fBCS 395T: Machine Learning\fP
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\fBWhen & Where:\fP Spring, 1990, Tues. & Thurs., 11:00-12:30, WEL 2.306
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\fBCourse Number:\fP 43105
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\fBProfessor:\fP Ray Mooney
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\fBOffice\fP: 4.138 Taylor, Phone: 471-9558, Email: mooney@cs.utexas.edu
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\fBOffice Hours:\fP 10-11 Mon. & Wed or by appointment
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\fBTeaching Assistant\fP: 
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\fBTA Office Hours\fP: 
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\fBPrerequisites:\fP CS 381K & CS 351 (or equivalent)
.uh "Course Overview"
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The intent of this course is to present a broad introduction to Machine
Learning in Artificial Intelligence including discussions of each of the major
approaches (see the course syllabus).  The primary focus of the course will be
on understanding the underlying algorithms used in various machine learning
systems.  Class lectures will discuss general issues in Machine Learning as
well as present abstract algorithms.  Implemented versions of each of these
algorithms will be provided in order to give a feel for how the systems
discussed in class "really work" and allow for experimentation.
.pp
Since there is currently no appropriate text, a collection of articles from
various books, journals, and conference proceedings will serve as reading
material for the course (see the syllabus for the list of papers).  A packet
of all of the reading assignments for the semester is available at Kinko's
Copies (2346 Guadalupe).  Another packet is available containing copies of the
transparencies I will use during class lectures (these are from last year and
may be amended somewhat during the course of the semester).
.uh "Course Requirements"
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Assigned reading should be read before the corresponding class and students
should be prepared to participate in class discussion about the material.  If
discussion is weak and I feel that papers are not being read, I reserve the
right to institute a policy of handing in written reactions to the reading
material.
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The graded work in the course will consist of about 5 or 6 homework
assignments (about one every two weeks) as well as a final project.
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The homework assignments will generally be programming assignments which
involve experimenting with, extending, and/or adding features to an existing
machine learning "micro-system".  These micro-systems will be written in
Common Lisp; consequently, adequate knowledge of Lisp programming is required.
Accounts on the departmental HP workstations (2.148 and 2.144 TAY) will be
supplied for doing assignments and projects.  If students have access to other
Common Lisp machines they are welcome to use them.  Note: Be sure to hand all
assignments in on time since 25% will be deducted each day an assignment is
late.
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The final project can be a more ambitious experiment or enhancement involving
existing micro-systems or an additional micro-system implementation.  In
either case, the implementation should be accompanied by a short paper
(7-10 double-spaced pages) describing the project.  Time permitting, a
class presentation can be given in lieu of a paper.
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All work in the class should be done individually and independently, including
the final project.
.uh "Grading"
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The final grade will be computed as follows:
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 55%  Homeworks (about 10% per assignment)
 35%  Final Project
 10%  Class Participation
\(em\(em
100%
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