Data and Decision Science Network Meeting - Machine Learning Taster

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A gentle introduction for any researchers curious about what machine learning is (and isn't)

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Presenter: Professor Alberto Nettel-Aguirre, UOW

The taster will be an introduction to machine learning. Topics covered include:

What do we mean by Machine learning? (main concepts, types: supervised/unsupervised)

Is more complex necessarily better? (Trade-offs of bias and variability regarding complexity)

Don’t overfit: Training-Testing data, What is cross-validation and how it works, why use it?

Nutshell intro (by example) to some of the most “common” supervised learning techniques: Classification and regression trees, K-nearest neighbours, support vector machines.

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