Master’s studies

Syllabus for Machine Learning

Maskininlärning

Syllabus

  • 10 credits
  • Course code: 1DT071
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1N, Technology A1N
  • Grading system: Fail (U), 3, 4, 5.
  • Established: 2010-05-06
  • Established by: The Faculty Board of Science and Technology
  • Revised: 2015-04-27
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: week 26, 2015
  • Entry requirements: 120 credits including 15 credits in mathematics and 60 credits in computer science/information systems, including 20 credits in programming/algorithms/data structures.
  • Responsible department: Department of Information Technology

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 subject of the project assignment is open for the students to define themselves, but must be approved before the work begins.

Instruction

Lectures, lab assignments and a self-defined project assignment.

Assessment

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.

Other directives

This course cannot be included in the same degree as 1DT022 or 1DT646.

Reading list

Applies from: week 26, 2015

  • Engelbrecht, Andries P. Computational intelligence : an introduction

    2. ed.: Chichester: Wiley, 2007

    Find in the library