- Code COMP8600
- Unit Value 6 units
- Offered by Research School of Computer Science
- ANU College ANU College of Engineering and Computer Science
- Course subject Computer Science
- Areas of interest Artifical Intelligence
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]
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
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
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 - Intro Prog for Data Scientists .
Tuition fees are for the academic year indicated at the top of the page.
- Student Contribution Band:
- Unit value:
- 6 units
Offerings, Dates and Class Summary Links
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|
|2318||24 Feb 2020||02 Mar 2020||08 May 2020||05 Jun 2020||In Person||N/A|