Search Results
Your search for courses · during 23FA · tagged with STAT Elective · returned 3 results
-
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.
- Fall 2023
- Formal or Statistical Reasoning
- Computer Science 200 or 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)
-
CS 320.00 Fall 2023
- Faculty:Anna Rafferty 🏫 👤
- Size:34
- M, WLanguage & Dining Center 104 9:50am-11:00am
- FLanguage & Dining Center 104 9:40am-10:40am
-
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, supervised and unsupervised classification methods, and understanding and visualizing spatial data. We will use the statistics software R in this course.
- Fall 2023
- Formal or Statistical Reasoning Quantitative Reasoning Encounter
- Statistics 120, Statistics 230 or Statistics 250
-
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).
- Fall 2023
- Formal or Statistical Reasoning Quantitative Reasoning Encounter
- Statistics 230 and 250. Exposure to matrix algebra may be helpful but is not required