COURSE DESCRIPTION:
This is an introductory graduate AI course.
During the first part of the semester we will cover general
knowledge representation techniques and problem solving strategies.
Topics will include
semantic nets,
search,
intelligent agents,
game playing,
constraint satisfaction,
rule-based systems,
logic-based systems,
logic programming,
planning,
reasoning with uncertainty,
and probabilistic reasoning.
During the second part of the semester we will discuss three important
application areas in AI: machine learning, natural
language processing, and machine vision.
For the catalog description of this course
see the WPI Graduate Catalog.
CLASS MEETING:
Tuesdays 4:00-8:00 pm. April 2 - June 4, 2013.
Classrooms:
- Huron training room: April 2, May 21, and June 4.
- Mississippi training room: all other classes.
INSTRUCTOR:
Prof. Carolina Ruiz
TEXTBOOK:
RECOMMENDED BACKGROUND:
Familiarity with data structures and a recursive high-level language.
GRADES:
| Exam 1 | 20%
|
| Exam 2 | 20%
|
| Project | 25%
|
| Homework | 35% (7% each hw, lower hw grade will be eliminated)
|
| AI Showcase | 7% Extra Points added as a bonus homework
|
| Class Participation | Extra Points
|
Your final grade will reflect your own work and achievements
during the course. Students are expected to follow
WPI's Academic Honesty Policy.
Students are expected to read the material assigned to each
class in advance and to participate in class.
Class participation will be taken into account when deciding
students' final grades.
EXAMS
There will be a total of 2 exams. Each exam will cover the
material presented in class since the beginning of the course.
In particular, the final exam is cumulative.
HOMEWORK, PROJECT, AND SHOWCASE
Homework
There will be several, individual homework assignments during the semester.
The homework statements will be posted on the course webpage.
Each student should hand-in his/her own individual written homework solutions
at the beginning of the class when the homework is due, and
should be prepared to present and discuss
his/her homework solutions in class immediatly after.
Project
There will be one major course project. This project may consist of several
smaller parts.
A detailed description of the project will be posted to the course webpage
at the appropriate time during the semester.
Although you may find similar programs/systems available online or in the
references,
the design and all code you use and submit for you projects MUST be your own original work.
Showcase
Each student will select an existing AI system/application of his/her choice, and will give a 5-10 min. in-class presentation, describing this system in as much detail as possible, focusing on the AI features of the system. Students will take turns presenting throughout the term.
CLASS MAILING LIST
The mailing list for this class is:
This mailing list reaches the professor and all the students in the class.
Important: Note that this mailing list contains your WPI email address, NOT your Cisco email address.
CLASS WEB PAGES
The web pages for this class are located at
http://web.cs.wpi.edu/~ruiz/Courses/cs534/CiscoS13/
Announcements will be posted on the web pages and/or
the class mailing list, and so you are urged to check your WPI email and
the class web pages frequently.
WARNING:
Small changes to this syllabus may be made during the course
of the term.
ADDITIONAL SUGGESTED REFERENCES
See my list of additional
AI, Machine Learning, Data Mining, Statistics, Databases, Data Sets and other online resources.
OTHER AI RESOURCES ONLINE:
