Introduction to Machine Learning Workshop   

  • Date: January 9-12, 2018
  • Time: 8:30 am - 12 pm  (two sessions: 8:30 am - 10 am, 10:30 am - 12 pm)
  • Location: Sala C200Campus San Joaquín
  • Karianne Bergen   kbergen [at] stanford [dot] edu
  • Alexander Ioannidis   ioannidis [at] stanford [dot] edu 
(Please send emails to both instructors and include "ML Workshop" in your subject heading.)

About this Workshop

In this Introduction to Machine Learning Workshop, we will present the principles behind when, why, and how to apply modern machine learning algorithms. We introduce a framework for reasoning about how to apply various machine learning techniques, emphasizing questions of over­fitting/under-­fitting, regularization, 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), and independent component analysis (ICA). Supervised machine learning algorithms presented will include neural nets, deep learning, boosting, bagging, and random forests. Imputation, the lasso, and cross-­validation concepts will also be covered. 

This workshop is based on CME 250, a short course offered for credit at Stanford University.


The workshop assumes no prior background in machine learning. Previous exposure to undergraduate-level mathematics (calculus, linear algebra, statistics) and basic programming (e.g. R/Matlab/Python) will be particularly helpful.