An introduction to machine learning, Data and Decision Science Network seminar (all welcome).
Presenter: Professor Alberto Nettel-Aguirre, Centre for Health and Social Analytics, NIASRA, UOW.
When: 7 October
Time: 12.00 pm – 1.30pm
This is a general presentation for anyone curious about what machine learning is (and isn’t). New technologies are generating massive amounts of data for almost everything. There is a need to explore and analyse this data; hence, analyses are evolving and aiming at finding the complex relations. There are many techniques available, machine learning methods are some of these. This seminar will cover; 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). What to watch out for; overfitting. Checking your model; training-testing data. What is cross-validation? how it works and why you should use it? Introduction, by example, to some of the most common supervised learning methods ( classification and regression trees, K-nearest neighbours and support vector machines).
Contact firstname.lastname@example.org for further information on the seminar or the Data and Decision Science Network.