COMP4670 Introduction to Statistical Machine Learning

Later Year Course

Offered By	School of Computer Science
Academic Career	Undergraduate
Course Subject	Computer Science
Offered in	First Semester, 2010 and First Semester, 2011
Unit Value	6 units
Course Description	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.
Learning Outcomes	On satisfying the requirements of this course, students will have the knowledge and skills to: understand a number of models for supervised, unsupervised, and reinforcement machine learning describe the strength and weakness of each of these models understand the mathematical background 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
Indicative Assessment	Two written assignments (20% each); Examination (60%)
Workload	Thirty one-hour lectures
Areas of Interest	Computer Science
Requisite Statement	Some background in elementary statistics and probabilities, numerical algorithms, and programming experience.
Prescribed Texts	Bishop, Christopher M. Pattern Recognition and Machine Learning , Springer
Other Information	http://sml.nicta.com.au/Education/Teaching/IntroToSML

The information published on the Study at ANU 2010 website applies to the 2010 academic year only. All information provided on this website replaces the information contained in the Study at ANU 2009 website.