Concepts in Machine Learning
This four week course is designed to introduce attendees to central concepts in machine learning as well as examples of applications in biomedical research. Each one hour lecture will emphasize conceptual and practical aspects of machine learning paradigms, explore the foundations of underlying mechanisms, and look at current or potential applications through examples or case studies. The course assumes a strong foundation in basic statistics, but does not assume any prior coding experience. At the end of this course, you will be able to understand the core differences between different forms of machine learning and consider their application with respect to a variety of problem spaces. This course (or equivalent knowledge/preparation) is intended as a prerequisite for future courses covering machine learning skills in both R and Python.
Required software: This is not a coding-based class, so a laptop is not required. A mobile device (smartphone or tablet) will be needed for signing in to the course at the beginning of each session. The HackMD (interactive page used for sharing links and information) for this course is here: https://hackmd.io/@k8hertweck/conceptsML
- Week 1: Intro and Conceptual Overview; Machine Learning and Experimental Design
- Week 2: Case Study in Regression
- Week 3: Case Study in Classification
- Week 4: Case Study in Deep Learning and Transfer Learning
- Each week's materials are described in the script prefaced with the number of the week.
exercises/includes a file for each week representing both the aggregated in-class exercises as well as additional supplemental exercises for practice
solutions/includes the solutions for all files in
instructors.mdincludes information for instructors to facilitate teaching each lesson
hackmdio.mdis an archive of the interactive webpage used during lessons