Electrical Engineering MA, Deep Learning, 7.5 credits

Aim

The content of the course is about techniques for analyzing, creating and implementing deep learning models for image analysis and analysis of large measurement data series. The course covers basic properties of the multilayer perceptron, convolutional networks, feedback networks, as well as classification and regression with large data sets.

Course objectives

After completing the course, the student should be able to:
- describe the theory behind the different techniques for deep learning
- implement and analyze a deep learning model for a specific problem

Content

Theory of multilayer perceptrons, convolutional networks, feedback networks, as well as classification and regression with large data sets.
Methods for analyzing, creating and implementing deep learning models.
Case studies in image analysis and analysis of large amounts of data.

Entry requirements

Electrical Engineering MA, Applied Machine Learning, 6 credits, or 60 credits in Electrical Engineering or Computer Science including an introductory course in machine learning.

Selection rules and procedures

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

Teaching form

The teaching will be done through lectures and tutoring.

In addition to the scheduled teaching time, extensive self-study is required.

Examination form

I101: Implementation and analysis of a deep learning model, Assignment Report, 6 Credits
Grade scale: Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.

Q101: Theory, Test/Quiz, 1.5 Credits
Grade scale: Fail (U) or Pass (G)

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 are entitled to three examination opportunities within one year according to the examination format given in this version of the course syllabus. After the one-year period, the examination format given in the most recent version of the course syllabus applies.

Grading system

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