C S 470
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Introduction to Artificial Intelligence
Computer Science
College of Computational, Mathematical, & Physical Sciences
Course Description
Introduction to core areas of artifical intelligence; intelligent agents, problem solving and search, knowledge-based systems and inference, planning, uncertainty, learning, and perception.
When Taught
Fall, Winter, and Spring
Min
3
Fixed/Max
3
Fixed
3
Fixed
0
Title
Recognize problems that can use AI methods
Learning Outcome
from business, medicine, robotics, gaming, and machine learning
Title
Identify the component elements of the problem
Learning Outcome
Does it require basic control?
Does it involve uncertain reasoning?
Does it have a simple goal, sophisticated utility, or multiple attributes?
Does it require sequential choice or planning?
How may decision makers are involved?
Title
Formalize the problem in a way that is amenable to a solutio
Learning Outcome
What is the internal representation?
What performance criteria are relevant?
What is the nature of the environment?
What are the actuators?
What are the sensors?
What are the agent's utilities?
How is uncertainty represented?
Title
Be able to analyze, implement, and experiment with several A
Learning Outcome
Uninformed, informed, constraint-satisfaction, hill-climbing, and stochastic (e.g., genetic algorithms) search
Markov provesses, Bayes rule, Bayes nets, Hidden Markov Models, Grid Filters, and Kalman Filters
Expected utility theory
Multi-attribute utility theory
Sequential choice under uncertainty: value and policy iteration
Minimax solutions from game theory: alpha-beta pruning
Title
Analyze and communicate solution quality
Learning Outcome
Determine whether your solution is correct
Express the quality of your solution
Value the need to communicate your solution to others
