* Due to COVID-19, this year workshops will be offered online only
ICME offers a variety of summer workshops to students, ICME partners, and the wider community. This year's series of day-long workshops is happening from August 17-22, 2020, as detailed below. All workshops are from 9:00 am to 4:45 pm (made of several sessions separated by time for breaks).These are full-day workshops - you can register for one workshop per day only.Please check our website for updated course descriptions and instructor bios, and soon registration dates.
Introduction to Machine Learning
This workshop presents the basics behind understanding and using modern machine learning algorithms. We will discuss a framework for reasoning about when to apply various machine learning techniques, emphasizing questions of over-fitting/under-fitting, interpretability, supervised/unsupervised methods, and handling of missing data. The principles behind various algorithms—the why and how of using them—will be discussed, while some mathematical detail underlying the algorithms—including proofs—will not be discussed. Unsupervised machine learning algorithms presented will include k-means clustering, principal component analysis (PCA), multidimensional scaling (MDS), tSNE, and independent component analysis (ICA). Supervised machine learning algorithms presented will include support vector machines (SVM), lasso, elastic net, classification and regression trees (CART), boosting, bagging, and random forests. Imputation, regularization, and cross-validation concepts will also be covered. The R programming language will be used for occasional examples, though participants need not have prior exposure to R.
Prerequisite: undergraduate-level linear algebra and statistics; basic programming experience (R/Matlab/Python).
About the Instructor: Alexander Ioannidis earned his Ph.D. in Computational and Mathematical Engineering and Masters in Management Science and Engineering both at Stanford University. He is a postdoctoral fellow working on developing novel machine learning techniques for medical and genomic applications together with Carlos Bustamante, Chair of the Department of Biomedical Data Science at Stanford Medical School. Prior to Stanford he earned his bachelors in Chemistry and Physics from Harvard and a M.Phil from the University of Cambridge. He conducted research for several years on novel superconducting and quantum computing architectures at Northrop Grumman's Advanced Technologies research center. In his free time he enjoys sailing.