ICS 270A: Introduction to Artificial Intelligence

Winter Quarter, 1999


Grading policy

Your final grade for ICS 270 will be based on the two exams, reaction papers, your final project and class participation.

The final project will be worth about 50% of the final grade. The project grade includes two parts:

(1) Demo performance: system capabilities and oral communication skills
(2) Final report: content and written communication skills

Note that each part includes an assessment of your communication ability. It is possible to design and code a good system, but receive a low overall grade due to your poor ability at convincing me that you have built a good system. As described in the final project information sheet, these projects may be team (2 person) projects, but in extreme cases, I reserve the right to give different grades to the two team players.

Exams will be worth about 30% of your final grade. The final exam will be cumulative, but will nonetheless not count too much more than the midterm.

Reaction papers will be worth about 20% of your final grade. Reaction papers may not be handed in late, as the articles will be discussed in class on the due date (they will be collected at the end of class). Writing a very brief reaction paper, or one that is a simple summary, is better than writing none at all.

Class participation will be used whenever a student falls into the crack between two grades. If there is evidence that the student is motivated and has participated in class discussions, he or she will receive the higher grade.

Caveat Emptor: All grading is somewhat subjective; but nonetheless, all decisions will be final.

Caveat of Forgiveness: Student effort and improvement over the course does count. If concerned, feel free to discuss your progress with me during office hours.

Caveat on Plagiarism: This previous caveat of forgiveness does not apply to unambiguous cases of plagiarism. You should know both ICS policy and UC-Irvine policy on this subject.

In one equation:

Final grade = 0.5(Project grade) + 0.3(Total exam grade) + 0.2(average reaction paper grade) ± (fudge factor)


last updated Jan 2, 1999 by John Gennari