Introduction to Statistical Machine Learning COMP4670

Learning outcomes

More information may be available for enrolled students on the course website at http://sml.nicta.com.au/isml12.html

1. Understand a number of models for supervised, unsupervised, and reinforcement machine learning
2. Describe the strength and weakness of each of these models
3. Understand the mathematical background from Linear Algebra, Statistics, and Probability Theory used in these machine learning models
4. Implement efficient machine learning algorithms on a computer
5. Design test procedures in order to evaluate a model
6. Combine several models in order to gain better results
7. Make choices for a model for new machine learning tasks based on reasoned argument