CSc 215 -- Fall 2017
Artificial Intelligence

| Syllabus | Schedule | Instructor | Homework | Collaboration Policy |

Course Syllabus

Scott Gordon
RVR 5040, phone: 278-7946,

Office Hours
Wed 2-4 (RVR-3018)
Thurs 3-5 (RVR-5040)

Class Meetings
Mon/Wed 4:00-5:15

Course Overview
In this course we will study intelligence and the possibility of its realization on digital computers. A variety of techniques for modeling or simulating intelligent behavior will be explored, including their applications and their limitations. Topics will encompass a wide variety of areas including search techniques such as A* and minimax with alpha-beta pruning, hill-climbing, evolutionary computation and genetic programming, simulated annealing, two-player games, decision tree learning, constraint satisfaction, expert systems, logic programming including forward and backward chaining, propositional and first-order logic, knowledge representation and knowledge engineering, reasoning with uncertainty including fuzzy logic and Bayesian reasoning, neural networks, backpropagation, convolutional networks and deep learning, natural language processing, swarm intelligence and other topics. The nature of intelligence and its philosophical implications and associated ethical considerations will also be examined. 3 units.

Course Materials
Norvig and Russell - Artificial Intelligence, A Modern Approach, 3rd edition, Prentice Hall, 2009 (required).

Fully classified graduate standing in Computer Science or Software Engineering, or consent of instructor.

Important Dates
Monday Sep 4 Labor Day (campus closed)
Wednesday Oct 18 Midterm Exam
Thursday/Friday Nov 23-24 Thanksgiving (campus closed)
Wednesday Dec 13 Final Exam 3:00-5:00pm
The proposed outline of material to be covered appears in the course schedule. Students are expected to attend all lectures and to read the relevant sections of the text prior to lecture. Students are responsible for making arrangements to get notes from other students if they are absent.

Homework Assignments
Five homework assignments will be given, which will be graded. Most of the assignments will be individual, but there may also be team assignments. The assignments will generally involve problem-solving but may also require programming or learning and using some particular tool. Refer to the Homework and Project Guidelines for more information.

There will be one semester project. The goal of the project is to explore a particular AI technique in-depth, including a proposal, implementation, report, and class presentation. The projects will all be individual. For more information, refer to the Homework and Project Guidelines.

The examinations include material covered in lecture, homework material, and material covered in the relevant sections of the course text and other reading materials provided. There will be one midterm and a final exam. The final exam will be comprehensive. Taking exams at times other than scheduled is only done under extreme circumstances and must be arranged in advance with the instructor.

Exams are closed book. ONE 8.5x11 sheet of notes will be allowed for each exam, and it must contain only handwritten notes. The page of notes will be turned in with each exam, and will be returned shortly afterwards or available for return from the instructor.

Homework 35% (7% each)
Project 25%
Midterm 15%
Final Exam 25%

At the end of the semester, a final percentage will be calculated according to the above criteria. It will then be rounded to the nearest integer. Then, two grades will be assigned: first, a straight percentage grade according to the following scale:

  90-100   A
  85-89    A-
  80-84    B+
  75-79    B
  70-74    B-
  65-69    C+
  60-64    C
  55-59    C-
  50-54    D+
  45-49    D
  40-44    D-
  below 40 F
The second grade assigned will be based on a curve of the final point scores of all students.

The final grade will be the higher of the two assigned grades. That is, the percentage scale listed above is the minimum grade that a student will receive based on his/her percentage.

Incomplete Grades
University guidelines regarding the grade of Incomplete will be strictly adhered to. Incomplete grades will only be given only for extraordinary conditions beyond a student's control. Inability to keep up with the work due to an excessive course load, for example, is insufficient to warrant an Incomplete. A student who does not have a passing grade based on the work completed thus far at the time of the request is ineligible for an Incomplete.

Students on military reserve whose units go on active duty during or around the final exam period are eligible for an Incomplete.

| Scott Gordon | CSUS | CSc Dept | CSc 215 |