This workshop will give an overview of several modern supervised and unsupervised machine learning methods. We will discuss the advantages and limitations of each and explore what types of problems each is best suited to address.
Workshop format: Lecture and discussion
Target audience: This workshop will be most useful for people whose research has (or could have) at least some quantitative elements and who are interested in incorporating Machine Learning into their work. It might also be interesting for people not currently involved in such research but curious about how ML can be used in research more generally.
Knowledge prerequisites: A "big picture" concept of what Machine Learning entails, namely selecting an algorithm with a mathematically defined learning goal and then using data examples to adjust that algorithm's parameters in order to move towards this goal. Participants should also have an understanding of what sorts of data exist in their field or project and what kinds of questions they might want to answer with ML.
Hardware/software prerequisites: None
Learning objectives: Attendees will leave with an understanding of common ML algorithms, the types of data they require, and what types of problems they are best suited for. If time allows, we will spend time discussing and brainstorming specific project ideas from participants’ individual research.
Please register online at(link is external)www.princeton.edu(link is external)/training(link is external).(link is external) If you have any trouble registering, please contact email@example.com.