Introduction to machine learning workshop

Machine learning (ML) is a set of computational methods used to build an algorithm to find trends, make classifications, or make predictions, after being trained on a dataset. Examples in our daily lives include email spam filters, streaming recommendations, and automatic subtitle generation. This workshop introduces key machine learning terminology and concepts such as regression, classification, and clustering, while also discussing the limitations of this approach. Participants will be need experience with basic Python such as writing loops and functions.

• Wednesday 22 - Thursday 23 April, 9:30am-1pm
• Wednesday 14 - Thursday 15 October, 9:30am-1pm

Attendees will be able to:

• Describe the ML workflow and use core scikit-learn functions to train, test, and evaluate models.
• Apply supervised learning methods (e.g. regression, decision trees, SVMs) to real datasets.
• Use unsupervised techniques (e.g. clustering, PCA) to explore and reduce data.
• Identify key limitations and ethical concerns in applying ML to research problems.

View the online curriculum.

Python users of any experience level are encouraged to engage with their peers across the University research community, including workshop instructors and CeR staff, by joining Hacky Hour to post questions and exchange ideas.

Research Data Support Services
Email: researchdata@auckland.ac.nz