C S 470
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Introduction to Artificial Intelligence
Computer ScienceCollege 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. This fulfills the Intellectually Enlarging aim by providing a "broadening" perspective on how computational intelligence can be applied across diverse human disciplines.
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? This outcome is Intellectually Enlarging as it requires the "rigorous" mental discipline and analytical depth needed to understand the underlying structure of intelligent behavior.
Title
Formalize the problem in a way that is amenable to a solution
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? This supports the Character Building aim by requiring "sustained effort" and technical "integrity" in translating abstract goals into precise, functional implementations.
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 This advances the Intellectually Enlarging aim by bridging theoretical mathematics with practical engineering to master the "commonality of disciplines."
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 This advances the Character Building aim as students must practice "honesty" in reporting experimental results and "responsibility" in communicating the impact of their technical findings to others.
