COURSE DESCRIPTION:
This is an introductory, upper level, undergraduate AI course. We
will cover general knowledge representation techniques and problem
solving strategies, including agents, heuristic search, adversarial search and game playing,
logic-based systems, constraint satisfaction problems, and
planning. We will also study three important application areas in AI:
machine learning, machine vision, and natural language processing. The
role of these application areas in robotics will be discussed.
For the catalog description of this course
see the WPI Undergraduate Catalog.
RECOMMENDED BACKGROUND:
- CS 2102 Object-Oriented Design Concepts,
- CS 2223 Algorithms, and
- CS 3133 Foundations of Computer Science.
CLASS MEETING:
| Kabir's section (A01): | MoTuThFr | 12:00-12:50 pm, | KH116
|
| Ruiz's section (A02): | MoTuThFr | 1:00-1:50 pm, | SL104
|
PROFESSORS:
GRADUATE TEACHING ASSISTANTS (TAs):
Important: All CS4341 students are welcomed to attend any of the professors' and the TAs'
office hours, regardless of what CS4341 section they are registered for.
TEXTBOOK:
GRADES:
| 3 Exams | 60% (20% each)
|
| 3 Projects | 39% (13% each)
|
| Homework | 1%
|
| 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.
According to the
WPI Undergraduate Catalog:
"Unless otherwise indicated, WPI courses usually carry credit of 1/3 unit. This level of activity suggests at least 17 hours of work per week, including class and laboratory time."
Hence, you are expected to spend 4 hours per week actively participating in this course's lectures, and at least 13 hours per week working on this course outside the classroom.
BS/MS GRADUATE CREDIT
This course may be taken for BS/MS credit by students who are or plan to be in the BS/MS program.
Written permission from the professor is required. In order to receive BS/MS
credit, students need to:
(1) obtain a BS/MS form from the CS Office (FL 230);
(2) secure the professor's signature on this form during the first week of the term;
(3) select an advanced AI application during the first two weeks of the term, in consultation with the professor;
(3) prepare a short paper and presentation on this advanced AI application;
(4) deliver your presentation to the class during a lecture that matches well your topic; and
(4) secure a second signature from the professor at the end of the term certifying that you have completed all required grad-credit work.
EXAMS
Format
There will be a total of 3 exams.
Each exam will cover the
material presented in class since the beginning of the term.
In particular, the final exam is cumulative.
Exams will be in-class, closed-book, individual exams.
Collaboration or other outside assistance on exams is not allowed.
Check the course schedule for exam dates.
Makeups
Regarding makeup exams,
we follow Prof. Gennert's policy:
"Makeup and/or early examinations are not given except under the most
dire of circumstances, and then only with corroborating documentation.
Note well that neither oversleeping, forgetting to show up for an exam,
nor conflicting travel arrangements are considered dire circumstances."
PROJECTS & HOMEWORK
Homework
There will be several, individual homework assignments during the semester.
The homework statements will be posted on the course webpage.
Each student should submit his/her own individual written homework solutions
on Canvas by the homework due date, and
should be prepared to present and discuss
his/her homework solutions in class immediatly after.
Individual homework will not be graded, but submissions will be checked to
ensure that students have worked on the homework and will count toward the
final course grade.
Working on the homework will be the best way to prepare for the exams.
Projects
There will be three major projects throghout the term.
These projects may consist of several smaller parts.
A detailed description of each project will be posted to the course webpage
at the appropriate time during the term.
Though unlikely, you may find similar programs/systems available online
or in the references.
However, the design and all code you use and submit for you projects MUST be your own original work.
Teams
Students are expected to organize themselves in groups of exactly 3
students for each of the projects.
Students on each team must belong to the same CS4341 section
(i.e., Kabir's or Ruiz's section).
Each project may contain both an individual assignment and a group
assignment.
Groups don't need to be the same for all projects.
That is, you can change groups from one project to the next as long as you work
as part of a group of 3 students.
Submissions and Late Policy
Unless otherwise noted:
- Homework and projects must be submitted using Canvas.
- Late submissions will be penalized as follows:
- submissions received within 4 hours of the due time will be accepted with
a penalty of 10% off the maximum possible score.
- submissions received within 8 hours of the due time will be accepted with
a penalty of 30% off the maximum possible score.
- no submissions will be accepted passed 8 hours from the due time.
No exceptions.
See each homework and project description for details.
CLASS PARTICIPATION
Students are expected to read the material assigned for each
class in advance and to participate in class discussions.
Class participation will count toward students' final grades.
Class Discussion Forums:
The main digital venue for communication outside the classroom will be the CS4341 Discussion Forums provided by Canvas. To access these discussion forums,
go to Canvas, click on "Dashboard" or on "Courses", then select "INTRODUCTION TO ARTIFICIAL INTELLIGENCE CS4341-A17-A01", and then click on "Discussions" on the left hand-side bar.
Class Mailing List:
Although all questions about projects, exams, course materials, lectures, etc. should be posted on the Canvas discussion board, there are two mailing lists for this class
(replace XXXX with 4341 below), which must be used only for the purposes specified below:
- csXXXX-staff@cs.wpi.edu this mailing list reaches the two professors and the two TAs
Use this "csXXXX-staff" mailing list for questions that are specific to your own personal situation only.
- csXXXX-all@cs.wpi.edu this mailing list reaches the entire class: professors, TAs, and all students.
Don't use this "csXXXX-all" mailing list. This list will be used only by the professors and TAs to send general announcements to the whole class.
Important:
- Use the Canvas discussion forums described above to post any questions about course material, projects, exams, or assignments so that everyone benefits from the discussion - do NOT email general questions to the professor and/or TA and/or "-all" mailing list.
- Don't use csXXXX@cs.wpi.edu or csXXXX@wpi.edu as these email addresses don't exist.
Please make sure to read the Canvas CS4341 discussion forums and emails sent to the class mailing list constantly throughout the term so that you don't miss any important course information.
CLASS WEB PAGES
The web pages for this class are located at
http://web.cs.wpi.edu/~cs4341/a17/
Announcements will be posted on the web pages and/or Canvas and/or
the class mailing list, and so you are urged to check your WPI email,
Canvas, 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 our list of additional
AI, Machine Learning, Data Mining, Statistics, Databases, Data Sets and other online resources.
OTHER AI RESOURCES ONLINE:
