Machine Learning

2018/2019

Prerequisite/Recommended prerequisite for participation in the module

Basic knowledge in probability theory, statistics, and linear algebra.

Content, progress and pedagogy of the module

Objective
The course gives a comprehensive introduction to machine learning, which is a field concerned with learning from examples and has roots in computer science, statistics and pattern recognition. The objective is realized by presenting methods and tools proven valuable and by addressing specific application problems.

Learning objectives

Knowledge

  • Must have knowledge about supervised learning methods including K-nearest neighbor’s, decision trees, linear discriminant analysis, support vector machines and neural networks
  • Must have knowledge about unsupervised learning methods including: K-means, Gaussian mixture model, hidden Markov model, EM algorithm, and principal component analysis
  • Must have knowledge about  probabilistic graphical models, variational Bayesian methods, belief propagation, and mean-field approximation
  • Must have knowledge about Bayesian decision theory, bias and variance trade-off, and cross-validation.
  • Must be able to understand reinforcement learning

Skills

  • Must be able to apply the taught methods to solve concrete engineering problems
  • Must be able to evaluate and compare the methods within a specific application problem

Competences

  • Must have competencies in analyzing a given problem and identifying appropriate machine learning methods to the problem
  • Must have competencies in understanding the strengths and weaknesses of the methods

Type of instruction

The program is based on a combination of academic, problem-oriented and interdisciplinary approaches and organized based on the following work and evaluation methods that combine skills and reflection:

  • Lectures
  • Classroom instruction
  • Project work
  • Workshops
  • Exercises (individually and in groups)
  • Teacher feedback
  • Reflection
  • Portfolio work

Extent and expected workload

Since it is a 5 ECTS course module, the work load is expected to be 150 hours for the student

Exam

Exams

Name of examMachine Learning
Type of exam
Written or oral exam
ECTS5
AssessmentPassed/Not Passed
Type of gradingInternal examination
Criteria of assessmentAs stated in the Joint Programme Regulations.
http:/​/​www.engineering.aau.dk/​uddannelse/​studieadministration/​

Additional information

Elective course
On this semester two courses must be chosen out of three elective courses (total: 10 ECTS).

Facts about the module

Danish titleMaskinlæring
Module codeN-IRS-K3-3
Module typeCourse
Duration1 semester
SemesterAutumn
ECTS5
Empty-place SchemeYes
Location of the lectureCampus Esbjerg
Responsible for the module

Organisation

Study BoardStudy Board of Energy
FacultyFaculty of Engineering and Science