UCR CS 170: Introduction to Artificial Intelligence

Class objective:

Learn the basic principles and techniques that have been developed to address artificial intelligence, the problems for which they are applicable, and their limitations. Topics covered include search algorithms (including heuristic search), knowledge representation and reasoning, learning algorithms, and advanced topics (time permitting).

Time and Place:
Lecture TR 11:10 a.m. - 12:30 p.m. INTN 1006
Getting Help from the TAs.
My TAs use the "pull" mode of instruction. That is, I have told them not to attempt to "push" information to the students. Instead it the responsibility of the students to "pull" information from the TA by asking questions. If none of the students have any questions, my TAs are allow to simply read books or do their own work during lab hours. Labs are completely optional (except for two quick project demonstrations during the quarter). The quality of your lab experience will depend on you taking a proactive role.
Instructor : Eamonn Keogh.

Teaching Assistants:

Ben Smith smithb@cs.ucr.edu Office hours: TBA

Qiang Zhu qzhu@cs.ucr.edu Office hours: TBA

Getting Help from the TAs.
My TAs use the "pull" mode of instruction. That is, I have told them not to attempt to "push" information to the students. Instead it the responsibility of the students to "pull" information from the TA by asking questions. If none of the students have any questions, my TAs are allow to simply read books or do their own work during lab hours. Labs are completely optional (except for two quick project demonstrations during the quarter). The quality of your lab experience will depend on you taking a proactive role.

Lab Attendance Policy: Because I will allow the projects to be done in any computer language, lab attendance is not compulsory. The TAs will be in the lab in the first half-hour of advertised times, and will be happy to help with homeworks/projects etc. So long as you show up in that first hour, the TA’s will stay the entire lab time and help you, but if no one is in the lab one hour after the lab begins, the TA’s will leave the lab.

Here are all class notes/homeworks etc. Note that these files are subject to change up to 24 hours before I use them, make sure you have the latest version.

I am having problems with my account, in the meantime you can grab all the files in this single zip

Bring slides labeled "blind search"
Bring slides labeled "Heuristic Search"
Bring slides labeled "Adversarial search"
Bring slides labeled "optimizingsearch"
Bring slides labeled "Machine Learning1"
Bring slides labeled "Machine Learning2"
Bring slides labeled "Machine Learning3"
Bring slides labeled "Knowledge1" and "Knowledge1"
Bring slides labeled "Knowledge3"

Homework/Project due dates

HW 0 is due on...
HW 1 is due on ...
HW 2 is due on
HW 3 and HW 4 is due on
Project 1 is due
HW 5 is due
Project II is due on
Do HW 6, but don't hand it in.
HW 7

Web resources/Links:

Our text book: Artificial Intelligence: A Modern Approach.

General course features and policies (please read these carefully)

Material covered: You'll be responsible for learning material covered in lecture, in the textbook, and in lab. Lecture does not cover all required material alone.

Academic dishonesty: please don't cheat, O.K.? You can report cheating anonymously at: https://www.cs.ucr.edu/cheating/. Assignment submissions must represent your own original work. Copying from any sources (web, other books, past or current students, etc.) is not allowed.

Regrade policy: regrade requests must be submitted in writing and within one week of the distribution of the graded material. Grade-database errors should also be pointed out within one week of posting.

Cell phones: During lectures, lab sessions and visits to Dr. Keoghs office, you must turn off your cell phone.