Title: Tutorial — Basics of Clustering and Machine Learning for Single-Cell Data
Lecturer: Will Raymond
Lecturer Website: https://www.engr.colostate.edu/~munsky/
Lecturer Email: firstname.lastname@example.org
Learn the basic types of machine learning (Unsupervised, Supervised, Decision) and learn when each one should be applied.
Test out simple classifiers in Tensorflow on a standard dataset (Iris and oxford flowers 17)
Extend these classifiers and try them on a simulated nascent chain tracking dataset.
- Visualize the decision boundaries of a FFNN on some 2D data.
Will Raymond graduated in May 2017 from Colorado State University with a Bachelors in Biomedical Engineering and a Bachelors in Chemical and Biological Engineering.
Will has been a PhD student in Dr. Munsky’s lab since 2019 working on a myriad of projects. He is currently the lead developer on the rSNAPsim, an open source Python TASEP mRNA simulation package. In addition to that, he also works on implementing machine learning for nascent chain tracking fluorescent spot identification. Additionally, a recent area of focus has been modeling tRNA abundances and their effect on translation dynamics.
His primary area of interest is the intersection of machine learning, bioinformatics, and RNA biology. He is most excited about novel ncRNA discovery and advancements in RNA structural identification and disease classification.
Title: Basics of Clustering and Machine Learning for Single-Cell Data
Abstract: In this tutorial we will go over the provided colab notebooks and work with the simulated nascent chain tracking dataset. We will try out some supervised learning and then move on to progressively more complicated classification algorithms.
Suggested Reading or Key Publications:
Links to Relevant Software:
- Question 1
- Question 2