Discussed at meeting on 06 September 2018; notes taken by Libby Megna
Machine Learning - What is it, and what can it offer biologists?
Learning Objectives:
Definition: Machine learning enables computers to do tasks without being explicitly programmed for those tasks.
Neural networks are a type of machine learning, but it does not encompass everything
Machine learning includes:
Machine learning algorithms are ubiquitous for commerical applicatioins–e.g. Netflix, Facebook, email spam filters
Machine learning has lots of potential in biology: medical imaging (classifying MRI images or classifying cells), wildlife management (classifying camera trap images; e.g. paper from UW Comp Sci dept (Clune? sp?)
Cool guide: A visual introduction to machine learning (super cool visualizations)
Model often doesn't work on test data as well as it did on training data
Supervised vs. unsupervised machine learning
Classifying candy ("organisms") activity - discussion points:
Connections to biological problems
Applying machine learning to biology
No shortage of easy-to-use ML tools in R
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Random notes by Libby
I think this is a good read on trade-off between prediction power vs. simplicty/mechanistic understanding: Breiman 2001
Deep learning is just a neural net with more layers
From Liz: Here is the presentation PDF and the candy-based exercise. Note that these materials were prepared for a teaching demo I had to do for a job interview.