Subject description
Data science and machine learning represent an overlap of statistics, computer science and domain expertise. The combination of statistics and computer science, allows machine learning methodology to take advantage of the strengths of both areas yielding methods that have some advantages over statistical analysis and computational algorithms on their own.
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Note: Students who have completed INFO411 prior to studying STAT351 will not receive credit for STAT351. Students who complete STAT351 first may continue on to complete INFO411 and receive credit for both.
Enrolment rules
Pre-Requisite
Equivalence
INFO411 - Data Mining and Knowledge Discovery
STAT951 - Data Science and Machine Learning for Health and Social Sciences
Delivery
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Teaching staff
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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 various examples
3.
Demonstrate a practical understanding of unsupervised learning to perform and interpret different types of clustering.
4.
Correctly implement a set of supervised learning concepts and techniques.
Assessment details
Quizzes
Unsupervised Learning Tasks
Supervised Learning Tasks
Analysis Tasks
Work integrated learning
Foundational WIL:This subject contains elements of "Foundational WIL". Students in this subject will observe, explore or reflect on possible career pathways or a work-related aspect of their discipline.
Textbook information
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