Many students start PhDs at Curtin without even the most basic of computing and data skills. We collaborate with Software Carpentry and Data Carpentry to both teach basic lab skills and provide high-quality, domain-specific training covering the full lifecycle of data-driven research.
This workshop will introduce machine learning concepts through a mixture of lecture and hands-on coding. The aim is to teach a basic understanding of designing machine learning workflows for supervised and unsupervised learning approaches, classification and regression methods and model tuning.
Who: The course is aimed at postgraduate students and researchers as well as professional staff who would like to know more about ML. The course will provide a general overview and is designed to help inform participants about which ML techniques work for different types of data and problems.
Prerequisites: A working knowledge of Python and Jupyter notebooks is essential for this workshop. i.e. knowledge of basic data structures, operations and how to write scripts. No prior knowledge of machine learning is expected.
- Data preparation
- Exploratory data analysis
- Cross validation
- Learning curves
- Model tuning
- Dimensionality reduction