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Your search for courses · during 25SP · tagged with STAT Elective · returned 4 results
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CS 320 Machine Learning 6 credits
What does it mean for a machine to learn? Much of modern machine learning focuses on identifying patterns in large datasets and using these patterns to make predictions about the future. Machine learning has impacted a diverse array of applications and fields, from scientific discovery to healthcare to education. In this artificial intelligence-related course, we’ll both explore a variety of machine learning algorithms in different application areas, taking both theoretical and practical perspectives, and discuss impacts and ethical implications of machine learning more broadly. Topics may vary, but typically focus on regression and classification algorithms, including neural networks.
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CS 320.00 Spring 2025
- Faculty:Tom Finzell 🏫 👤
- Size:34
- M, WLanguage & Dining Center 104 11:10am-12:20pm
- FLanguage & Dining Center 104 12:00pm-1:00pm
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MATH 271 Optimization 6 credits
Optimization is all about selecting the "best" thing. Finding the most likely strategy to win a game, the route that gets you there the fastest, or the curve that most closely fits given data are all examples of optimization problems. In this course we study linear optimization (also known as linear programming), the simplex method, and duality from both a theoretical and a computational perspective. Applications will be selected from statistics, economics, computer science, and more. Additional topics in nonlinear and convex optimization will be covered as time permits.
- Spring 2025
- FSR, Formal or Statistical Reasoning
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Student must have completed any of the following course(s): MATH 134 – Linear Algebra with Applications or MATH 232 – Linear Algebra AND MATH 120 – Calculus 2 or MATH 211 – Multivariable Calculus with a grade of C- or better or equivalents.
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STAT 220 Introduction to Data Science 6 credits
This course will cover the computational side of data analysis, including data acquisition, management, and visualization tools. Topics may include: data scraping, data wrangling, data visualization using packages such as ggplots, interactive graphics using tools such as Shiny, an introduction to classification methods, and understanding and visualizing spatial data. We will use the statistics software R in this course.
- Spring 2025
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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Student has completed any of the following course(s): STAT 120 – Introduction to Statistics or STAT 230 – Applied Regression Analysis, or STAT 250 – Introduction to Statistical Inference with a grade of C- or better.
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STAT 320 Time Series Analysis 6 credits
Models and methods for characterizing dependence in data that are ordered in time. Emphasis on univariate, quantitative data observed over evenly spaced intervals. Topics include perspectives from both the time domain (e.g., autoregressive and moving average models, and their extensions) and the frequency domain (e.g., periodogram smoothing and parametric models for the spectral density). Exposure to matrix algebra may be helpful but is not required.
- Spring 2025
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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Student has completed any of the following course(s): STAT 230 and STAT 250 with a grade of C- or better.