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Your search for courses for 23/SP and in CMC 210 found 4 courses.
CS 321.00 Making Decisions with Artificial Intelligence 6 credits
Open: Size: 34, Registered: 0, Waitlist: 0
| M | T | W | TH | F |
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| 11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |
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There are many situations where computer systems must make intelligent choices, from selecting actions in a game, to suggesting ways to distribute scarce resources for monitoring endangered species, to a search-and-rescue robot learning to interact with its environment. Artificial intelligence offers multiple frameworks for solving these problems. While popular media attention has often emphasized supervised machine learning, this course instead engages with a variety of other approaches in artificial intelligence, both established and cutting edge. These include intelligent search strategies, game playing approaches, constrained decision making, reinforcement learning from experience, and more. Coursework includes problem solving and programming.
Prerequisite: Computer Science 201. Additionally Computer Science 202 is strongly recommended.
MATH 342.00 Abstract Algebra I 6 credits
Open: Size: 25, Registered: 0, Waitlist: 0
| M | T | W | TH | F |
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| 9:50am11:00am | 9:50am11:00am | 9:40am10:40am |
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Prerequisite: Mathematics 236 or instructor permission
POSC 170.00 International Relations and World Politics 6 credits
Open: Size: 30, Registered: 0, Waitlist: 0
| M | T | W | TH | F |
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| 1:15pm3:00pm | 1:15pm3:00pm |
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STAT 250.00 Introduction to Statistical Inference 6 credits
Open: Size: 30, Registered: 0, Waitlist: 0
| M | T | W | TH | F |
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| 12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |
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(Formerly Mathematics 275) Introduction to modern mathematical statistics. The mathematics underlying fundamental statistical concepts will be covered as well as applications of these ideas to real-life data. Topics include: resampling methods (permutation tests, bootstrap intervals), classical methods (parametric hypothesis tests and confidence intervals), parameter estimation, goodness-of-fit tests, regression, and Bayesian methods. The statistical package R will be used to analyze data sets.
Prerequisite: Mathematics 240 Probability (formerly Mathematics 265)
Formerly Mathematics 275
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