Search Results
Your search for courses · during 26WI · tagged with STAT Elective · returned 3 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.
X seats held for CS Match until the day after X priority registration.
- Winter 2026
- FSR, Formal or Statistical Reasoning
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Student has completed any of the following course(s): CS 200 with a grade of C- or better or CS 201 with a grade of C- or better or received a Carleton Computer Science 200 Requisite Equivalency AND CS 202 with a grade of C- or better or received a Carleton Computer Science 202 Requisite Equivalency or MATH 236 with a grade of C- or better or received a Carleton Math 236 Requisite Equivalency. MATH 236 will be accepted in lieu of CS 202.
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CS 320.01 Winter 2026
- Faculty:Anna Meyer 🏫 👤
- Size:28
- M, WWeitz Center 233 9:50am-11:00am
- FWeitz Center 233 9:40am-10:40am
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28 seats held for CS Match until the day after Senior priority registration.
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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.
- Winter 2026
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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Student has completed any of the following course(s): STAT 120 or STAT 230, or STAT 250 with a grade of C- or better.
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STAT 260 Introduction to Sampling Techniques 6 credits
Covers sampling design issues beyond the basic simple random sample: stratification, clustering, domains, and complex designs like two-phase and multistage designs. Inference and estimation techniques for most of these designs will be covered and the idea of sampling weights for a survey will be introduced. We may also cover topics like graphing complex survey data and exploring relationships in complex survey data using regression and chi-square tests.
- Winter 2026
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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Student has completed any of the following course(s): STAT 120 or STAT 230, or STAT 250 with a grade of C- or better.