Subject description
Data science and machine learning represent an overlap of statistics, computer science and domain expertise and are increasingly becoming integral in research applications based on health and social data. The combination of statistics and computer science, allows machine learning methodology to take advantage of the strengths of both areas yielding … For more content click the Read More button below.
These methods are not a panacea. A broad understanding of data science and machine learning methods not only improves research capacity of the individual researcher, it also means the health and social professional is able to interpret correctly and make decisions from research using common supervised and unsupervised methods such as linear and logistic regression, classification and regression trees, neural networks, support vector machines and clustering.
Equivalence
STAT351 - Fundamentals of Data Science and Machine Learning
INFO911 - Data Mining and Knowledge Discovery
Delivery
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Learning outcomes
On successful completion of this subject, students will be able to:
1.
Describe the fundamental concepts of statistical and machine learning, including cross-validation.
2.
Determine whether supervised or unsupervised methods are appropriate for use in health and social science examples.
3.
Demonstrate a practical overall understanding of unsupervised learning to perform and interpret different types of clustering.
4.
Correctly implement a suite of supervised learning concepts and techniques.