Electrical Engineering MA, Applied Machine Learning, 7.5 credits

Aim

To give an introduction to several sub-areas within machine learning and to inform about basic methods and algorithms available in these areas. To convey breadth and depth in machine learning and its application in measurement technology.

Course objectives

After the course the student should be able to:
-Apply methods to import, combine, annotate and convert data to appropriate formats for data analysis,
-Explain the benefits of data mining and select and implement the appropriate method in typical use cases,
-Select, motivate and apply common methods and algorithms for machine learning for typical use cases and present the results in an appropriate way,
-Design and perform performance validation for machine learning systems,
-Assess the applicability of machine learning in measurement systems
-Describe ethics around the selection of training data and using statistical methods for making conclusions

Content

-Unsupervised and supervised learning, classification and regression
-Neural networks including convolutional networks, recurring neural networks and deep learning
-Bayesian learning
-Support vector machines, KNN, K mean clustering, linear classifiers, decision trees, random forests, ensemble methods
-Machine learning in measuring systems

Entry requirements

Degree of Bachelor of Science, Degree of Bachelor of Science Engineering (at least 180 credits), or equivalent, with at least 60 credits in Electrical Engineering or Computer Science.

Selection rules and procedures

The selection process is in accordance with the Higher Education Ordinance and the local order of admission.

Teaching form

The work effort for the entire course normally comprises 200 hours. This means that in addition to the scheduled time, the student must complete extensive self-study. The number of teaching hours for the specific course opportunity is defined in the schedule. Alternatively, the course is given in distance form without physical meetings and instead with web-based material. In this case, you need your own computer with administrator rights and internet connection to be able to follow the course. Teaching can be in Swedish or English.

Examination form

I101: Machine learning , Written Assignment, 3 Credits
Grade scale: Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.

I201: Machine learning for measurement systems , Written Assignment, 3 Credits
Grade scale: Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.

I301: Literature review and ethics , Written Assignment, 1.5 Credits
Grade scale: Fail (U) or Pass (G)

The final grade is based on the performance in module I101 and module I102.

Grading criteria for the subject can be found at www.miun.se/gradingcriteria.

The examiner has the right to offer alternative examination arrangements to students who have been granted the right to special support by Mid Sweden University’s disabilities adviser.

Examination restrictions

Students registered on this version of the syllabus have the right to be examined 3 times within the course of 1 year according to the specified examination forms. Thereafter, the examination form according to the latest current version of the syllabus applies.

Grading system

Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.