Introduces fundamental principles of artificial intelligence, including heuristic search, automated reasoning, handling uncertainty, and machine learning. Presents applications of AI techniques to real-world problems such as understanding the web, computer games, biomedical research, and assistive systems. This course is a prerequisite for advanced AI courses.
I would add that this course will require programming. Plenty of programming. If this is not your strength, please leave extra time to work on assignments and projects. There is no official lab section to this course. We are happy to help, but you must make the time to do the work and seek help as needed.
Note that class was moved from CSB601 to B&L106 starting Tue Jan 22.
| Vedant Ahuwalia | vahluwal at u dot rochester dot edu |
| Seong Bang | sbang3 at u dot rochester dot edu |
| Amsal Karic | akaric at u dot rochester dot edu |
| Simon Luppescu | sluppesc at u dot rochester dot edu |
| Matt Overlan | m dot overlan at rochester dot edu |
| Andrew Wong | awong16 at u dot rochester dot edu |
Additional help available from your very own Computer Science Undergraduate Council (CSUG): CSUG Tutoring
Detailed syllabus (subject to change)
Homework is entirely optional. If you do it (on time), we will grade it and you will get feedback. If you don't do it, it's up to you to know whether you know the material or not. Late homework will not be graded. Because the homework is optional, it will not count towards your final grade (see below).
projectseven though
mini-projectmight be more accurate. There is one project for each unit of the course. The dates are listed on the syllabus but you usually have about two weeks to do them. Projects will be submitted in BlackBoard also.
The projects will involve programming. I strongly urge you not to leave them until the last minute. Every year several people learn that AI programs are as hard as AI problems. Projects will also involve a written report, which will count for 50% of the grade on the project.
The purpose of the quizzes is not to punish you, but to let you know if you understand the material well enough. Seriously. I will spend too much time grading these for it to be for my benefit.
Your grade in the course will be calculated as follows (subject to change):
| Homework | 0% |
| Assignments and projects | 40% |
| Poster Session | 10% |
| Quizzes | 10% |
| Midterm | 20% |
| Final | 20% |
So the grade is about half developing and describing AI programs, and about half formal exams, with 10% for our almost-end-of-year poster session.
Your letter grade will follow the University of Rochester Grading Scheme. The following table is an estimate of how the numeric grades will map onto the letter grades (subject to change):
| Letter | Meaning | Number |
|---|---|---|
| A | Excellent | ≥90% |
| B | Above Average | ≥75% |
| C | Minimum Satisfactory Grade | ≥60% |
| D | Minimum Passing Grade | ≥50% |
| E | Fail | <50% |
Note that the University scheme puts average
somewhere between
C and B. This mapping from numbers to letters is subject to change,
but you get the idea: Getting an A requires truly excellent
work. Even a B is above average
. Mind you, as
in Lake Wobegon, I'm
sure all CSC242 students are above average.
Last update: Thursday, 28-Feb-2013 12:14:25 EST