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!
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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.
What You Will Learn
- Express why Statistical Learning is important and how it can be used.
- Identify the strengths, weaknesses and caveats of different models and choose the most appropriate model for a given statistical problem.
- Determine what type of data and problems require supervised vs. unsupervised techniques.
Syllabus
WEEK 1
Statistical Learning Introduction
Introduction to overarching and foundational concepts in Statistical Learning.
WEEK 2
Accuracy
Exploration into assessing models in different situations. How do we define a "best" model for given data?
WEEK 3
Simple Linear Regression
Introduction to Simple Linear Regression, such as when and how to use it.
WEEK 4
Multiple Linear Regression
A deep dive into multiple linear regression, a strong and extremely popular technique for a continuous target.
WEEK 5
Classification Overview
WEEK 6
Classification Models
