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.

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

Assignment 1 (20%); Assignment 2 (20%); Final Oral Exam (60%)

Thirty one-hour lectures

Some background in elementary statistics and probabilities, numerical algorithms, and programming experience.

Bishop, Christopher M. Pattern Recognition and Machine Learning , Springer

http://sml.forge.nicta.com.au/isml.html