Catalog Description: Intelligence, rational agent design, state-spaces, search, heuristic functions and A*, game playing, knowledge representation, formal logic, reasoning, expert systems, machine learning paradigms including neural networks and genetic algorithms, introduction to Lisp and Scheme. Programming projects and review of literature (3 units).

Learning Outcomes:

After successful completion of this course, students will be able to:

1. author creative and robust programs in Lisp or Scheme

2. analyze the strengths and limitations of various state-space and heuristic search algorithms

3. represent domain knowledge in propositional and first-order logic

4. utilize probabilistic reasoning techniques to solve problems with incomplete or uncertain information

5. apply machine learning techniques to real-world problems

Meetings: asynchronous on-line. 

Required Text: Artificial Intelligence: A Modern Approach (3^rd Edition) by Stuart Russell and Peter Norvig. A paperback and online edition are available.

Prerequisites: C- or better in CS 301.

Canvas: All course materials will be posted to Canvas. This includes all lectures, slideshows, homework assignments and programming projects.

Assignments: Problem sets will be assigned periodically throughout the semester. All homework must be submitted via Canvas by the posted deadline. Problems must be in proper order! No late homework will be accepted for any reason. 

Students are allowed (and actively encouraged) to form study groups, and to submit collective assignments as a team.

This course has a reader who assists the professor in grading student submissions.

Projects: In addition to problem sets, you will also be completing several programming projects throughout the semester.

This course will develop a student’s understanding of the Lisp, Scheme and Prolog programming languages. No prior experience with these languages is necessary. All projects must be authored in the specified programming language. Several Common Lisp listeners, Scheme platforms and Prolog interpreters are available online as a free download; any will suffice for this course.

All projects must be submitted via Canvas by the posted deadline. The submission must contain source code, any required data files, and instructions for running the project (if necessary). Any project that does not load or run (even if it does nothing) will automatically receive a grade of zero.

As with assignments, students are allowed to submit collective projects as a team.

This is an upper division CS course. It is not the role of the instructor to debug student programs.

Examinations: This course is taught as a seminar. As such, there are no exams. Instead, there is a final project that it due on the Monday of exam week.

Academic Dishonesty: By enrolling in this class, the student agrees to uphold the standards of academic integrity described in the catalog at http://www.csueastbay.edu/ecat/current/i-120grading.html#section12. Although collaborate study and dialogue are encouraged, students are expected to author solutions entirely on their own. Any evidence of cheating will be pursued to the fullest extent of university policy. 

Important Dates:

· January 22^nd – January 26^th  faculty on strike (could be extended…)

· January 29^th  last day to drop course without permission

· February 5^th  having surgery (no office hours)

· April 1^st  Cesar Chavez Holiday (campus closed)

· April 2^nd – April 5^th Spring Break (campus closed)

· April 12^th  last day to withdraw from class

· May 1^st last day of classes

· May 6^th  Final Project is due

Grading: 50% assignments, 50% projects. No extra credit will be assigned. Grades will not be adjusted in any way - so an 89.9% is still a B+. No incomplete grades will be given. The grading scale is as follows:

A 92.5%

A- 90.0%

B+ 87.5%

B 82.5%

B- 80.0%

C+ 77.5%

C 72.5%

C- 70.0%

D+ 67.5%

D 60.0%

F lower than 60%