CSE 473 Introduction to Artificial Intelligence
Mon/Wed/Fri 10:30-11:20 am, EE 003
666 Paul J. Allen Center
Office hours (held in CSE 666)
and by appointment
Office hours (held in CSE 212)
and by appointment
Textbook: Artificial Intelligence, A Modern Approach: Second Edition.
Stuart Russell & Peter Norvig. Prentice Hall, 2003. Please note
that you must have the Second Edition.
CSE 326 required, CSE 341 recommended. Not open for credit to students
have have completed CSE 415.
This course will enable you to:
- Represent real-world constraint satisfaction problems in terms of state-space
search, and solve them using a variety of different search algorithms.
- Represent and solve deductive reasoning problems using propositional logic.
- Represent and solve probabilistic reasoning problems using Bayesian networks.
- Use machine learning algorithms, including neural networks and decision
trees, to solve classification problems such as arise in medicine and science.
- Understand how perception, learning, and reasoning interact in an autonomous
A set of assignment will be given out (almost) every Monday. The due
date for each task will be specified. It is important that you finish each
task by its due date, because doing the task prepares you for the work we do in
the classroom that day. There will be 5 different kinds of tasks:
- Readings: Read the assigned pages from
the texbook and write notes that
summarize the material. The notes may be as short or long as you like,
typically one side of a sheet of paper. The readings and dates
due are all specified on the Calendar
page. On the indicated date turn in a short summary of the readings at the
start of class. These will be checked off as having been done, and
returned to you. For the final exam you may bring these notes plus any
others you take during or after class.
- Written Exercise: A paper and pencil type exercise. Answers may
be handwritten or typed. These will be graded.
- Programming Problem: These tasks will involve implementing a program
and testing it on a given set of data. Programming tasks should be done in
teams of two. I will prepare the assignments with a Java implementation in
mind, but you are free to use a different programming language (for example,
C++, LISP, ML, Prolog, or Python). Data sets and support code (if any) will be
provided on the instruction Unix servers. You may transfer these materials
to a Windows machine and work in that environment if you prefer.
Programming tasks will be graded on correctness, documentation and
understandability of the code, and write-up of the experimental results.
- Application of Software Tools: These tasks will involve learning to use
a AI software tool (for example, the C4.5 decision tree learning system)
and analyzing the result of using the tool on a set of test data. These tasks will be graded on
correctness and the write-up.
70% of your grade will be based on the assignments and
30% on the midterm and
final exam. See the Grade
Breakdown page for the formula used to calculate the final grades.
The midterm exam will be in class on Nov 5. The final
exam is 8:30 am - 12:20 pm on Friday, Dec 12.
Staying in Touch
The course mailing list is <firstname.lastname@example.org>,
and will be enabled by Oct 3rd. Please use it as a resource to discuss
issues about the course and the assignments with other students. The TA
and instructor will also be reading this list, so it is also appropriate for any
question of a general nature. You may also send email to Jeffrey or me
directly with either general questions or questions about your own performance
in the course. The course home page is:
I encourage people to come to office hours. You do not have to wait
until you have a problem with an assignment: I am happy to talk about your
progress in the undergraduate program, career and graduate school opportunities,
or wild and crazy ideas about AI. I will be out town at computer science
research conferences Sept 30-Oct 3, Oct 20, and Nov 10-12. Jeffrey and
guest lecturers will fill in on these days.
Collaboration and Academic Honesty
In this class, you will be collaborating with other students to solve
problems. Some solutions will be handed in and graded as a group activity and
some will be individually written and evaluated. When doing individual
assignments, you may still collaborate with other students. However, you must
give credit to others who have given you some insight into solving a problem,
just as professionals recognize others who have helped with their research or