- Code COMP8600
- Unit Value 6 units
- Offered by School of Computing
- ANU College ANU College of Engineering Computing & Cybernetics
- Course subject Computer Science
- Areas of interest Artifical Intelligence
- Academic career PGRD
- Prof Lexing Xie
- Mode of delivery In Person
- Co-taught Course
First Semester 2023
See Future Offerings
In 2023, this course is on campus with remote adjustments only for participants with unavoidable travel restrictions/visa delays.
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 modelling; 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
Information on inherent requirements are currently not available.
Requisite and Incompatibility
Prescribed TextsBishop, Christopher M. Pattern Recognition and Machine Learning , Springer
Students are expected to have a mathematics background that is equivalent to MATH1014 or MATH1115, and a computer science background equivalent to COMP1110 or COMP1140 or COMP7230.
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
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
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- Domestic fee paying students
- International fee paying students
Offerings, Dates and Class Summary Links
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Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|2219||20 Feb 2023||27 Feb 2023||31 Mar 2023||26 May 2023||In Person||View|