About the course
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).
Alexander graduated from Harvard in Chemistry and Physics and earned an M.Phil in Computational Biology and Diploma in Greek from the University of Cambridge. He has a Ph.D. in Computational and Mathematical Engineering from Stanford, where he teaches machine learning and data science. Prior to S...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.