Course Policies and Procedures
Introduction to Artificial Intelligence / CS440
Wed, 5:00pm –
cs440 is an introductory course about the field known as Artificial Intelligence. Artificial Intelligence is not only about building an “artificial mind”, a robot or the world’s best chess playing program. We will learn how to mathematically model (simple) tasks in real world that deal with perception and action: how a computer may use video cameras and gyroscopes to autonomously fly a helicopter, how to design algorithms that search through the web looking for pieces of information that exactly answer our questions, and how to understand those questions in the first place. To do this we will need to know how to conveniently (from computational perspective) represent our knowledge about those tasks, how to infer best actions (motor torque) based on the perceived state of the world (helicopter’s camera seems to “see” a tree in front of the helicopter), and how to systematically learn further facts about those tasks from the knowledge and percepts.
Course Web Site We will be using Rutgers' E-companion online course site, www.rutgersonline.net. Each registered student should have received access information for RutgersOnline. All notices and assignments will be posted on RutgersOnline. Homework assignments are to be turned in through RutgersOnline Dropboxes. Please get familiar with the site. Let me know if you have any problems accessing the site.
Therefore, if you turned in your assignment at 2:10am the day after the due date and you solved all problems correctly, you will get a score of 80 instead of 100. See Grading Policy for further details.
There will be two tests in the course of this class: a midterm test (late-october) and a final test. Both tests are closed books, no notes or cheatsheets.
All your work in the course should be governed by the Rutgers and CS Dept. Policies on Academic Integrity. This, among other things, means that no dishonesty, no cheating on tests, etc. is allowed. Please respect your fellow students' work!
You final grade will be based on how well you perform on homeworks, midterm and final exams. Each performance item will weigh according to the table below.
Each time you turn in an assignment (or a test) you will be given a numeric score S between 0 (worst) and 100 (best). The score will then be converted to a normalized score:
Sn = 3/2 ( S - E[S] ) / StdDev[S] + 4
truncated to the range [0,7]. This, for instance, means that if you score 60 points on the final, the class average is 50 and the standard deviation is 30, your normalized score will be 4.5. The normalized score will correspond to the letter grades in the table below. Hence, in the example above 4.5 would correspond to letter grade B.
Final course grade will be assigned based on the weighted average score of all assignments and tests:
FSn = 0.3 [ Sn(HW1) + ... Sn(HWN) ] / N + 0.3 Sn(Mid) + 0.4 Sn(Fnl).
After computing the final score I may adjust it based on my overall impression of your performance as well as make adjustments in case of multimodal score distributions. Your final course letter grade will be computed from the final numeric score FSn and the table above.
This is a way to use Mozilla/Firefox with RutgersOnline: