WSP 365 — Introduction to Machine Learning (ICME Workshop)
Big data has changed the way we work, live, and play. Data science—developing and testing models and algorithms—helps us gain knowledge for ourselves and provide insights to others.
Introduction to Machine Learning is one of ten workshops included in Fundamentals of Data Science, a series of one-day workshops offered by the Stanford Institute for Computational and Mathematical Engineering (ICME). Fundamentals of Data Science provides an introduction to multiple aspects of data science for those who are new to the field and those seeking to broaden their education and skills in data science. Students can sign up for one workshop, or several throughout the week. Students who complete four workshops will qualify for the Stanford ICME Fundamentals of Data Science Summer Workshops Certificate of Completion.
These workshops are not eligible for tuition discounts through Stanford Continuing Studies, but ICME offers discounts for eligible affiliates and partners. See below for details:
Stanford staff and full-time Stanford students: You may be eligible for a tuition discount if you register directly through ICME. The tuition for Stanford staff is $100 and the tuition for full-time Stanford students is $75. For more information and to register with these discounts, visit https://sto.stanfordtickets.org/icme2020/homepage.
ICME partners and affiliates qualify for a discount. If you think you qualify and have not received a discount code separately, email email@example.com. You will need to register through ICME's website.
Workshop: Introduction to Machine Learning
Instructor: Alex Ioannidis, Postdoctoral Scholar, Biomedical Data Sciences, Stanford Medical School
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).
Please note: Although the enrollment limit for this workshop is set to 60 Continuing Studies students, this course is designed for the entire Stanford community, and enrolled Continuing Studies students will be joined in the classroom by Stanford graduates and undergraduates. Students should expect a large class.