Mathematics for Smart Automation (5 ECTS)

Objective

The course includes the basic mathematical skills needed for courses in data processing, artificial intelligence and machine vision. Upon completion of the course, students will have mastered the basics of probability and statistics with emphasis on practical applications, as well as essential concepts of linear algebra.

Content

• Probability and statistics
• Linear algebra

Location and time

On-campus classes according to the course schedule.

Materials

Material presented in lectures, online sources, and literature introduced in the course.

Teaching methods

Lectures
Weekly exercises
Exams

Exam schedules

Exam dates will be announced at the beginning of the course.
Retakes or missing assignments due to absences will be arranged separately.

Student workload

Approximately 135 hours in total, including about 40 hours of lectures and classes, and about 70 hours for weekly assignments and exercises.

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

The student
• has achieved the objectives of the course to a satisfactory level
• is able to identify, define and use concepts and models in the subject area of the course
• understands the conditions and principles of the development of expertise
• has completed the learning tasks required for the course to the minimum standard
• has developed their competences in such a way that they will be able to complete their future professional studies and eventually work in the field.

Assessment criteria, good (3)

The student
• has achieved the objectives of the course well, although there are still areas where knowledge and skills need to be improved
• has completed the learning tasks of the course at a satisfactory or good level
• has a good understanding of the concepts and models of the subject matter of the course and is able to carry out a reasoned analysis
• is able to apply what they have learned in learning and working life situations
• understands the importance of expertise in the field and is able to analyse their own expertise.

Assessment criteria, excellent (5)

The student
• has achieved the objectives of the course with excellent marks
• has completed the learning tasks of the course at a good or excellent level
• has an excellent command of the concepts and models of the subject matter of the course
• is able to analyse clearly and reasonably and propose practical development measures
• has a good ability to apply what they have learned in learning and working life situations
• is able to analyse expertise in their field and their own development towards expertise.

Assessment methods and criteria

The course assessment will be discussed during the first class.
Performance in exercises and exams will affect the final grade.