Spring 2020: Machine Learning
BMERC is sponsoring a student-led cross-disciplinary journal club focused on machine learning, with particular applications in biomedical and drug discovery research. Over the past years machine learning has revolutionized the field of biomedical research. The goal of this journal club series is to introduce fundamental concepts of machine learning to life scientists and engineers, and thus encourage cross-disciplinary conversations and promote application of cutting edge machine learning methods in future research. You can find Jupyter Notebook sessions and Power Point slides on our dedicated github repository. The schedule of sessions is listed below:
Due to the recent developments related to the coronavirus (COVID-19) the first journal club of the semester, originally scheduled for March 18th, will be POSTPONED to a later date (TBD).
— TBD —
Topic: Applications of ML in protein engineering
Description: We will cover a specific application of machine learning in protein engineering. Discussion of the selected paper will be led by BME student Jeffrey McMahan of the Ngo lab.
— September 18, 2019 —
Topic: Supervised Learning
Tutorial: Scikit-learn: SVM, Random Forest Examples
Description: We will explore the fundamentals of supervised learning using the selected reference paper. After a 20 to 30 minute presentation, a 5-10 min Q&A session will follow. Then, we will have a 20-30 min hands on session on Jupyter where we will use readily available data from scikit-learn to classify using SVM, Random Forest or other supervised ML method of participants’ choosing.
— October 23, 2019 —
Topic: Unsupervised Learning
Tutorial: Scikit-learn: Clustering (K-means) and Classification (Nearest Neighbor)
Description: We will explore the fundamentals of unsupervised learning using the selected reference paper. After a 20 to 30 minute presentation, a 5-10 min Q&A session will follow. Then, we will have a 20-30 min hands on session on Jupyter where we will use readily available data from scikit-learn to cluster or classify using any unsupervised ML tool of the participant’s choice.
— November 20, 2019 —
Topic: Assessment of ML/DL Models
Tutorial: ROC Curve, Specificity, Selectivity, Accuracy, etc.
Description: We will explore the most common ways of evaluating machine learning models built in the first two sessions. Using the reference paper (TBD) and scikit-learn Model Evaluation, we will have a short presentation and Q&A session followed by a longer (30-40 min) hands-on session.
— December 11, 2019 —
Topic: BME/Chemistry Application of ML
Description: On the last day of ML journal club for the semester, we will have an application focused session. A volunteer participant will put together a 20-30 min presentation followed by either a hands-on session (if the algorithm/ML tool from the paper is available) or a discussion session where participants share how the algorithm/ML tool can be used in their research field.