- From www.coursera.org
Advanced Linear Models for Data Science 1: Least Squares
- Self-paced
- Free Access
- Fee-based Certificate
- 6 Sequences
- Advanced Level
Course details
Syllabus
- Week 1 - Background
We cover some basic matrix algebra results that we will need throughout the class. This includes some basic vector derivatives. In addition, we cover some some basic uses of matrices to create summary statistics from data. This includes calculating and subtrac... - Week 2 - One and two parameter regression
In this module, we cover the basics of regression through the origin and linear regression. Regression through the origin is an interesting case, as one can build up all of multivariate regression with it. - Week 3 - Linear regression
In this lecture, we focus on linear regression, the most standard technique for investigating unconfounded linear relationships. - Week 4 - General least squares
We now move on to general least squares where an arbitrary full rank design matrix is fit to a vector outcome. - Week 5 - Least squares examples
Here we give some canonical examples of linear models to relate them to techniques that you may already be using. - Week 6 - Bases and residuals
Here we give a very useful kind of linear model, that is decomposing a signal into a basis expansion.
Prerequisite
Instructors
Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health
Editor
Johns Hopkins University (JHU) is a private American university located in Baltimore, Maryland. It also has campuses in Washington, D.C. Bologna, Italy, Singapore and Nanjing, China. It owes its name to Johns Hopkins, a wealthy entrepreneur who bequeathed 7 million dollars to the university on his death.
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Platform
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