Introduction to Statistical Machine Learning COMP6467

Learning outcomes

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

On satisfying the requirements of this course, students will have the knowledge and skills to:

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. define the mathematical objects from Linear Algebra, Statistics, and Probability Theory used in these machine learning models
4. implement these machine learning models 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