Caught up in Neural Nets? When (and How) to use Classical Machine Learning in Your Research

In this workshop participants will learn the basics of various classical machine learning techniques and discuss which types of problems each technique is best suited to address.

Workshop format: Lecture and discussion

Target audience: This workshop will be useful for those who are interested in incorporating machine learning into their research and are either unfamiliar with it or have experience with neural networks but are unfamiliar with classical machine learning.

Knowledge prerequisites: No previous knowledge of machine learning is required. Participants should be familiar with the types of data that exist in their area of research and have examples of problems they are interested in solving in mind.

Hardware/software prerequisites: None

Learning objectives: By the end of the workshop the participants will (1) become familiar with four broad categories of classical machine learning: classification, clustering, regression, and dimensionality reduction; (2) understand the difference between supervised and unsupervised learning algorithms; and (3) be introduced to the types of problems and data that each category of algorithms is best suited for.

Organized by the Princeton Institute for Computational Science and Engineering (PICSciE) and OIT Research Computing. This event is co-sponsored by the Center for Statistics and Machine Learning (CSML).

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Co-hosted with: GradFUTURES

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