Syllabus for Natural Computation Methods for Machine Learning
Naturliga beräkningsmetoder för maskininlärning
- 10 credits
- Course code: 1DL073
- Education cycle: Second cycle
Main field(s) of study and in-depth level:
Computer Science A1N
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2018-03-08
- Established by:
- Revised: 2018-08-30
- Revised by: The Faculty Board of Science and Technology
- Applies from: Spring 2019
120 credits including 15 credits in mathematics and 60 credits in computer science/information systems, including 20 credits in programming/algorithms/data structures. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Responsible department: Department of Information Technology
On completion of the course, the student should be able to:
- describe how, and why, natural computation methods work, explain principles and show examples.
- set up and solve typical 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,
- recognize typical effects of bad choices (problem setup and parameter selection, for example) and determine how the results can be improved based on this,
- plan an open project so that it can be implemented within the given limits.
The course introduces various natural computation methods. The course is divided into a theoretical part and a practical part.
The theoretical part consists of lectures and literature on various topics, including (but not limited to):
- learning paradigms (supervised, unsupervised and reinforcement learning),
- artificial neural networks for classification, function approximation and clustering,
- deep learning,
- reinforcement learning and temporal difference learning,
- evolutionary computing (genetic algorithms and genetic programming), and
- swarm Intelligence (ant colony optimisation, particle swarm optimisation).
The practical part consists of lab assignments and a self-chosen project task. The subject of the project assignment is defined by the students themselves, but must be approved by the course teacher before the work begins.
Lectures, labs and project.
Written exam (4 credits), written and oral examination of assignments (6 credits).
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
The course cannot be included in the same degree as 1DT071, 1DT022, or 1DT646
- Latest syllabus (applies from Spring 2019)
- Previous syllabus (applies from Autumn 2018)
The reading list is missing. For further information, please contact the responsible department.