### Learning outcome

### Contents

### Required prerequisite knowledge

### Recommended prerequisites

In the course the different methods are communicated by presenting and explaining the mathematical details. It is recommended that students who wish to follow the course should have solid prior mathematical knowledge especially in linear algebra and statistics. There is a lot of emphasis on the laboratory part of the course where one uses Scientific Python. Those who follow the course should therefore also have good programming skills, and must be prepared to write functions using iterative control structures and think about code reuse.

### Exam

Weight | Duration | Mark | Supporting materials | |
---|---|---|---|---|

Written exam | 1/1 | 4 hours | A - F | No printed or written materials are allowed. Approved basic calculator allowed. |

### Coursework requirements

### Course teacher(s)

- Course coordinator
- Trygve Christian Eftestøl
- Course teacher
- Ketil Oppedal
- Coordinator laboratory exercises
- Jarle Urdal
- Head of Department
- Tom Ryen

### Method of work

It is important to work with theoretical and practical exercises to be able to solve real world problems.

### Overlapping courses

Course | Reduction (credits) |
---|---|

Pattern recognition (MIK190_1) | 10 |

### Open to

Industrial Automation and Signal Processing - Master's Degree Programme - 5 year

Robot Technology and Signal Processing - Master's Degree Programme

### Course assessment

### Literature

Last updated: 13.08.2020