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 09:40 a.m. - 11:00 a.m. PRCE 3374
Laboratory M 08:10 a.m. - 11:00 a.m. ENGR2 135
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.

Homeworks

Both homework 0 and homework 1 are due on the 13th of April, at the beginning of class. However, you must read the two homeworks carefully by April 8th. I may ask questions in a pop quiz that assume you have read them by the 8th.

The order we will study the slides are...

Bring slides labeled "blind search"
Bring slides labeled "Heuristic Search"
Bring slides labeled "Adversarial search" April 8th.
Bring slides labeled "optimizingsearch" April 13th
Here are the review of search slides
Bring slides labeled "Machine Learning1" April 20th
Bring slides labeled "Machine Learning2"
Bring slides labeled "Machine Learning3"
Bring slides labeled "Knowledge1" and "Knowledge1"
Bring slides labeled "Knowledge3"

Instructor : Eamonn Keogh.

Teaching Assistants:

Curtis Yu <cyu@cs.ucr.edu> Office hours are Mon/Wed from 1 to 2pm at ENGR2 226.

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. If you want the TA to be in the lab, you should email him with 24 hours notice.

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

Web resources/Links:

Our text book: Artificial Intelligence: A Modern Approach.

Project Two

The data is here

You only need to download two files, one small and one large. Download the files that have your number, see list below.

For example, Calvin Chang will download cs_170_Large4.txt and cs_170_Small4.txt

You may find the following fact useful to test your algorithm. On large dataset 1 the error rate can be 0.940, when using only features 6 40 35.

On small dataset 1 the error rate can be 0.943, when using only features 9 6 3.

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.