CS2013-final-report

CS 662; Artificial Intelligence Programming, University of San
Francisco
Christopher Brooks
cbrooks@usfca.edu
https://sierra.cs.usfca.edu
Knowledge Areas that contain topics and learning outcomes covered in the course
Knowledge Area
Total Hours of Coverage
Intelligent Systems (IS) 60
Where does the course fit in your curriculum
The course is taken as a senior-level elective, and as a required course for first-year Master’s students.
What is covered in the course
An overview of AI, including search, knowledge representation, probabilistic reasoning and decision making
under uncertainty, machine learning, and topics from NLP, information retrieval, knowledge engineering and
multi-agent systems.
What is the format of the course
Face-to-face. 4 hours lecture per week (either 3x65 or 2x105).
How are students assessed
Two midterms and a final. Also, heavy emphasis on programming and implementation of techniques. Students
complete 7-9 assignments (see website for examples). Expectation is 3 hours outside of class for every hour of
lecture.
Course textbooks and materials
Russell and Norvig’s Artificial Intelligence: A Modern Approach is the primary text. I prepare lots of summary
material (see website) and provide students with harness code for their assignments in Python. I also make use of
pre-existing packages and tools such as NLTK, Protégé and WordNet when possible.
Why do you teach the course this way
My goals are:
• Illustrate the ways in which AI techniques can be used to solve real-world problems. I pick a specific
domain (such as Wikipedia or a map of San Francisco) and have the students apply a variety of
techniques to problems in this domain. For example, as the assumptions change, the same SF map
problem can be used for search, constraints, MDPs, planning, or learning.
• Provide students with experience implementing these algorithms. Almost all of our students go into
industry, and need skill in building systems.
• Illustrate connections between techniques. For example, I discuss decision trees and rule learning in
conjunction with forward and backward chaining to show how, once you’ve decided on a representation,
you can either construct rules using human expertise, or else learn them (or both).
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