Introduction to Artificial Intelligence
Introduction to Artificial Intelligence
Introduction to core areas of artifical intelligence; intelligent agents, problem solving and search, knowledge-based systems and inference, planning, uncertainty, learning, and perception.
| Hours | 3.0 Credit, 3.0 Lecture, 0.0 Lab |
| Prerequisites | C S 312 & MATH 215 & STAT 121; or C S 312 & MATH 215 & STAT 201; or C S 312 & MATH 313 & STAT 121 |
| Note | Students are allowed only 1 retake of CS 355. This includes students who have failed or withdrawn (received a "W" grade). If after 1 retake, a student needs to retake the course again, the student must wait 1 semester/term before being allowed to take any C S course and must follow the petition process at cs.byu.edu/retake-policy. This policy does not apply to classes dropped before the add/drop deadline. Petitions for exceptions to the policy can be completed at cs.byu.edu/retake-policy. |
| Taught | Fall |
| Programs | Containing C S 470 |
Course Outcomes:
Recognize problems that can use AI methods
from business, medicine, robotics, gaming, and machine learning
Identify the component elements of the problem
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?
Formalize the problem in a way that is amenable to a solutio
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?
Be able to analyze, implement, and experiment with several A
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
Analyze and communicate solution quality
Determine whether your solution is correct
Express the quality of your solution
Value the need to communicate your solution to others