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https://github.com/alexmclain/bios_735

Biostatistics for Public Health

https://github.com/alexmclain/bios_735

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Biostatistics for Public Health

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  • Host: GitHub
  • Owner: alexmclain
  • Language: HTML
  • Default Branch: main
  • Size: 36.6 MB
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Created 11 months ago · Last pushed 10 months ago
Metadata Files
Readme

README.md

Biostatistics 735

Machine Learning for Public Health Applications

For course notes, handouts, and examples see the Class-Material folder. For homework, paper, and project information see the Assingments folder. For general course information see the syllabus.

Overview

This course introduces the core concepts and practical applications of machine learning (ML) for public health, biostatistics, and related disciplines (e.g., epidemiology, psychology, neuroscience, genetics). Working in R, you will learn how to prepare data, select and implement appropriate ML methods, and evaluate model performance. The emphasis is on conceptual understanding, hands-on implementation, and interpretation, not on mathematical derivations. By the end of the course, you will be ready to incorporate ML techniques into your own research, with particular attention to the unique challenges and ethical considerations of health data.

The main topics we will cover in this course (among others) are:

  • Penalized Regression (Shrinkage Methods)

  • Tree-Based Methods: CART and Ensemble Techniques

  • Support Vector Machines

  • Neural Networks

  • Basics of Large Language Models

  • Dimension Reduction

  • Clustering

Owner

  • Name: Alexander McLain
  • Login: alexmclain
  • Kind: user
  • Location: Columbia, SC
  • Company: University of South Carolina

I have a broad range of interests that focus on estimation, prediction and inference in complex models, commonly with clustered or high-dimensional data.

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