Learning outcomes and course content

Learning outcomes

Upon completion of the course, the student should be able to

1. explain, derive, and implement a number of models for supervised, unsupervised learning,
2. explain how various models and algorithms relate to one another,
3. describe the strengths and weaknesses of various models and algorithms,
4. select an appropriate model or approach for a new machine learning task.

Course content

Machine Learning is the study of algorithms that can improve their
performance through experience. Experience usually takes the form of
data such as labelled and/or unlabelled examples. Machine learning
algorithms are used in a vast number of application domains and
tasks. However, to do this successfully a machine learning
practitioner must have a systematic understanding of how to learn to
perform a required task from data.

It is the goal of this course to give you this understanding. We will
present a set of machine learning algorithms and statistical modelling
techniques. But more importantly you will learn how the different
algorithms are developed, how they are related, and how and when they
should be used both in theory and practice.