COMP 341
Fall 2022
| Section:
01
02
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This course is an introduction to creating programs that appear to behave intelligently. Topics will include search algorithms for problem solving, as well as probabilistic reasoning, including regression, classification, and decision making. Sample topics include Bayesian networks, basic neural networks and reinforcement learning. |
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| Credit: 1 |
Gen Ed Area Dept:
NSM MATH |
| Course Format: Lecture | Grading Mode: Graded |
| Level: UGRD |
Prerequisites: COMP212 AND MATH228 |
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Fulfills a Requirement for: (COMP)(IDEA-COMP)(STS) |
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Past Enrollment Probability: 50% - 74% |
| SECTION 01 |
| Instructor(s): Wolfe,Pippin Times: .M.W.F. 09:50AM-10:40AM; Location: SCIE141; |
| Total Enrollment Limit: 30 | | SR major: 15 | JR major: 10 | | |
| Seats Available: 0 | GRAD: X | SR non-major: X | JR non-major: X | SO: 5 | FR: X |
| Drop/Add Enrollment Requests | | | | | |
| Total Submitted Requests: 3 | 1st Ranked: 0 | 2nd Ranked: 2 | 3rd Ranked: 0 | 4th Ranked: 0 | Unranked: 1 |
| SECTION 02 |
| Instructor(s): Wolfe,Pippin Times: .M.W.F. 10:50AM-11:40AM; Location: SCIE184; |
| Total Enrollment Limit: 30 | | SR major: 15 | JR major: 10 | | |
| Seats Available: 15 | GRAD: X | SR non-major: X | JR non-major: X | SO: 5 | FR: X |
| Drop/Add Enrollment Requests | | | | | |
| Total Submitted Requests: 1 | 1st Ranked: 0 | 2nd Ranked: 0 | 3rd Ranked: 0 | 4th Ranked: 0 | Unranked: 1 |
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