Essential foundations for any machine learning application are a basic statistical analysis of the data to be processed, a solid understanding of the mathematical foundations underpinning machine learning as well as the basic classes of learning/adaptation concepts. Those foundations are bundled in this single, introductory course to machine learning in preparation for deeper explorations into the topic, but also as a standalone unit.
Upon successful completion, students will have the knowledge and skills to:
- Develop an appreciation for what is involved in learning from data
- Understand basic data wrangling and data exploration
- Describe a variety of machine learning tasks: clustering, dimensionality reduction, regression and classification
- Understand how to formalise practical problems in terms of above tasks
- Interpret mathematical equations from linear algebra, calculus, statistics, and probability theory in terms of machine learning methods
- Understand how to perform evaluation of and reflect on learning algorithms and model selection in practical problems
- Assignment (40) [LO null]
- Final Exam (60) [LO null]
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Up to 60 hours of total face-time, which includes interactions with lectures and tutors. Up to 60 hours of total preparation, repeat, assignment and practical exercise time.
Requisite and Incompatibility
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
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- 6 units
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