Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more!
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
Overview
Syllabus
- Statistical Learning Introduction
- Introduction to overarching and foundational concepts in Statistical Learning.
- Accuracy
- Exploration into assessing models in different situations. How do we define a "best" model for given data?
- Simple Linear Regression
- Introduction to Simple Linear Regression, such as when and how to use it.
- Multiple Linear Regression
- A deep dive into multiple linear regression, a strong and extremely popular technique for a continuous target.
- Classification Overview
- Classification Models
Taught by
James Bird
