This course has been adjusted for remote participation in Semester 1, 2022.
This course provides a broad but thorough introduction to the methods and practice of statistical machine learning. Topics covered will include Bayesian inference and maximum likelihood modeling; regression, classification, density estimation, clustering, principal and independent component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimisation; overfitting, regularisation, and validation.
Upon successful completion, students will have the knowledge and skills to:
- Describe a number of models for supervised, unsupervised, and reinforcement machine learning
- Assess the strength and weakness of each of these models
- Interpret the mathematical equations from Linear Algebra, Statistics, and Probability Theory used in these machine learning models
- Implement efficient machine learning algorithms on a computer
- Design test procedures in order to evaluate a model
- Combine several models in order to gain better results
- Make choices for a model for new machine learning tasks based on reasoned argument
- Exam 1 (20) [LO 1,2,3,7]
- Exam 2 (20) [LO 1,2,3,7]
- Final Exam (60) [LO 1,2,3,5,6,7]
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Workload2 lectures, 1.5 hours each (3 hours total per week, 1 lab session (2 hours) per week, 2 hours independent study per week
Requisite and Incompatibility
Bishop, Christopher M. Pattern Recognition and Machine Learning , Springer
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.
- Student Contribution Band:
- Unit value:
- 6 units
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