Administrative Details
CS B652: Computer Models of Symbolic Learning
(This information is subject to change)
Staff
Professor
David Leake
Office: Info West 203
(inside the 205 suite). When you arrive for office hours, please
knock, even if someone is with me, so I will know you are there.
Email: leake at
indiana.edu
Appointments (in addition to
office hours): For appointments, please email my assistant
Michele Dompke, mdompke at Indiana.edu. Occasionally I have to
attend meetings on short notice; if I have to miss office office
hours, please email me with your constraints and I’ll be glad to set up
a makeup meeting as soon as possible.
Associate instructor
Eriya Terada
Email: eterada at
indiana.edu
Office hours
Office hours are held
when classes are in session. Dates with adjustments are noted
below.
Mon (note: No class or
office hours Mon 1/18, Martin Luther King Day)
9:30-10:30am,
David Leake, IW203
10:30am-12:00pm,
Eriya Terada, IW205 (Eriya will begin office hours when the first homework is
assigned)
Wed
10:30am-12:00pm, Eriya Terada,
IW205 ((Eriya will begin office hours when the first homework is assigned)
Fri
11am-1:00pm,
David Leake, IW203
Prerequisites
B551, or a similar introduction to artificial intelligence, or permission
of the instructor.
To do well in the course and to benefit fully from the material it's
essential to have the AI equivalent of what mathematicians call
"mathematical maturity," for at least a semester of AI, to be able
to bring that to bear on assessing problems and in designing and executing
the projects.
Homeworks may assume knowledge of B551 topics. Homework programming will
be in python. Projects may be programmed in other languages with
permission of the instructor/AI.
Textbook
There is no required textbook, but portions of the course
will draw on Mitchell’s Machine
Learning, and Russell & Norvig's Artificial Intelligence: A Modern
Approach, Third edition, Prentice Hall, 2009, may be useful. There are
copies on 2-hour reserve at Swain Library. Many readings will be from
current papers.
Homework and in-class
exercises
Homeworks may be due as soon as the next class after they
are assigned. Consequently, if you must miss a class, be sure to check
promptly with others in the class about the material covered and any
assignments you missed. There may also be some short in-class exercises
during the semester, done and submitted during the class session and/or
graded canvas discussions.
Class Participation
Students will be expected to participate actively in class
discussions.
Exams
There will be 1-2 in-class exams. No make-up exams
will be given except for extreme circumstances and if permission is granted before
the exam.
Academic Integrity
Homeworks, including programming projects, are to be completed
individually unless specified as a group assignment. You may discuss the
material with other students, but all written work (code, etc.) must be
your own and must be written independently. You may not share
anything written, show copies of code, or take written notes/code away from
your discussions. You may not consult online code resources or other sources
concerning the solutions to homework problems.
Any interactions with other students concerning an assignment or use of
materials beyond Russell and Norvig or class assignments or resources must
be documented in your submission. If you clearly document the
resources you use and how they were used, credit may be adjusted for
parts not fully your own.
Please discuss any questions on this with us before submitting your
assignments.
Homework Submission, Due Dates, Lateness
Policies
Homework assignments are due by the start of class on the due date unless
otherwise specified. Assignments will normally be submitted on Canvas. Be sure to verify after
submission that you have uploaded the desired file!
Be sure to keep a copy of the submission until it is graded and you
have verified the score recorded on canvas. You must notify us of any
problems within a week of posting of grades.
Some homework assignments may be discussed the day of submission, and
those will not be accepted late (this will be stated on those
assignments). For other assignments, each student will start the
semester with 3 “lateness days”, each allowing a submission up to 24 hours
late without penalty. We recommend conserving these for when they’re
needed for other classes have similar deadlines or unexpected
circumstances. After those are used, assignments allowing late
submission may be submitted up to 48 hours late, with a penalty of 15% per 24
hours.
Missed in-class exercises: There will not be make-ups for in-class
work, but the lowest 20% (approximately) of the in-class exercises will be
dropped from the final grade calculation.
If you have to miss a class, you are still responsible for any readings
and homeworks assigned there, so be sure to check with other students to find
out what material you missed.
Default Grading Scale
The default grading scale is the IU oncourse scale:
Grade
Minimum %
A+ 97.0
A
93.0
A-
90.0
B+ 87.0
B
83.0
B-
80.0
C+ 77.0
C
73.0
C-
70.0
D+ 67.0
D
63.0
D-
60.0
F
0.0
Email Questions
Email questions can be sent to either the instructor or
AI. Please begin the subject line with "B652". Email will be
responded to within 24 hours, unless otherwise noted (absence due to travel,
etc.). Please allow sufficient time for responses before assignment
deadlines. The AI will be the primary support for questions on
programming assignments.
A note about artificial
intelligence and the goals of the course
As we will see throughout the course, AI is not a field in
which there is a set of neatly defined problems to solve, nor one in which it
is always obvious what constitutes the "right" solution.
Consequently, learning about AI involves not only learning about methods but
also developing a viewpoint on what constitutes an AI question, how to define
AI questions, which AI questions to explore, and how to recognize good
answers. Rather than simply learning the approaches, we will be thinking
critically about their goals and methods, analyzing their strengths and
weaknesses, and attacking AI problems to find ways to improve them.
This makes it an exciting and challenging area that may be quite different
from what you are used to studying in other courses.
This also makes studying AI a good preparation for
attacking real-world problems: A programmer or consultant's first task is
often to determine the key goals and to decide which method(s) to bring to
bear, before designing and implementing a system.
Program Expectations
The programs that you write for assignments should be
designed to apply to a broad class of examples beyond those stated.
Programming assignments will specify a few test cases on which to demonstrate
your program, but your solutions should apply to a broad class of examples
beyond those stated. This is described more completely in the on-line
handout ``Guidelines
for programming assignments.'' Please ask if you are unsure about the
level of generality for a specific portion of the code.
Programs should be well-documented program and
submitted with output demonstrating their processing.
Semester Project
A major part of the course will be a semester research
project involving developing a knowledge-based system, evaluating your
results, writing a paper on that research, and presenting your work in a
course “mini-conference”. This is expected to be a significant project going
deeply into an area. For some past students, this project has produced
published conference papers and long-term research topics continuing beyond
the class. Projects will be done in small groups (normally 2 students).
Each student should contribute a clearly identifiable portion of the
project.
Students interested in AI are strongly encouraged to
select projects that will provide a basis for future AI research. Students
not intending to focus on AI are encouraged to select projects that are
relevant to their research interests in other areas. Your final project
can be an excellent addition to the portfolio of accomplishments you can show
prospective employers.
During the semester, students will prepare brief progress
reports on their projects.
Paper Presentations and
discussants
The class will be divided into groups to examine a topic
in current knowledge-based AI research, critically analyze it, present it to
the class, and lead a class discussion. Each student will participate in one
of these groups.
The jumping-off point for the class session will be a
paper that the entire class will read. However, the presenters will go
beyond that to do a deeper analysis, bring in additional material, argue for
their own view of the material, and propose how it could be improved. The
paper presentation must go well beyond simply summarizing the paper and must
include leading class discussion. Members may be graded individually, if
necessary due to major differences in contributions.
For each presentation, some students not in the presentation
group will be assigned as discussants. The discussants will make a
brief presentation after the main presentation, giving their own ideas and
responding to the points raised in the presentation. These written comments
will be read by the instructor and passed on to the presenters. Other
students will be asked to submit constructive feedback on the presentations
to be shared with the presenters.
Calculation of Final
Grades
Homework and in-class exercises will count 20%, the
exam(s) will count 20%, in-class presentations and related work will count
25%, and the final project (including presentations and written materials)
will count 35%.
For group work, team members may be asked to provide a
course-grained assessment of the team members' relative contributions and/or
to have an oral exam on their code. It is expected that different members may
make different types of contributions, but overall contributions are expected
to be equivalent, for each team member to receive the same project score. If
the instructor determines that there were significantly different levels of
contribution, relative contributions may be used to weight the distribution
of points within a project group.
Special accommodation
Students who need any special accommodation must contact
the professor during the first week of class to discuss arrangements.
Incompletes
Incompletes will be handled in accordance with Computer
Science Department policy. In particular, incompletes will only be given to
students who have successfully completed most of the coursework, and who have
an acceptable reason for the incomplete. Unexpected difficulty with other
classes is not an acceptable reason for an incomplete.
Computer Use in Class
Students are asked not to do e-mail or other
computer work in class.
Other questions
If you have any questions that this doesn't cover, please
ask!
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