Entry requirements

120 credits including Basic algebra, Boolean logic, derivatives and the chain rule, vectors and matrices, programming and algorithms and data structures. Recommendations: Mathematical statistics, artificial intelligence, computer architecture.

Learning outcomes

After the course, the students will be able to

setup and solve typical machine learning problems, by implementation or by using established computer simulation tools.

decide which machine learning methods/algorithms are suitable for which type of learning problems, i.e. know about their most important weaknesses and advantages.

decide how to represent data to facilitate learning.

recognise typical effects of bad initialisation and parameter selection and suggest ways to improve the results.

describe how, and why, machine learning and natural computation methods work, explain principles and show examples.

Content

The course introduces various machine learning techniques, with a focus on natural computation methods. The course is divided into a theoretical part (4 credits) and a practical part (6 credits).

Theoretical part

The theoretical part consists of lectures and written course material on various topics, including (but not limited to)

Learning paradigms (supervised, unsupervised and reinforcement learning).

Artificial neural networks for classification, function approximation and clustering.

Reinforcement learning methods and Temporal Difference Learning.

Evolutionary computing (genetic algorithms, genetic programming).

Swarm Intelligence (ant colony optimisation, particle swarm optimisation).

Practical part

The practical part consists of lab assignments and a project assignment.

The lab assignments cover a pre-defined selection of topics from the theoretical part. The topics covered are presented at the beginning of the course, and on the course home page.

Towards the end of the course, the students work on a project assignment, the subject of which is open for the students to define themselves, but which must be approved before work begins.

Instruction

The course consists of lectures, lab assignments and a self-defined project assignment.

Assessment

The students are examined through a written or oral exam, and through written reports covering the lab assignments and the project assignment. The students are also required to present their results from the project assignment in seminar form. Details about the examination are presented at the beginning of the course, and on the course homepage.