Digital modelling (3cr)
Code: A800DB96-3001
General information
- Enrollment
- 13.11.2023 - 17.01.2024
- Registration for the implementation has ended.
- Timing
- 08.01.2024 - 25.02.2024
- Implementation has ended.
- Number of ECTS credits allocated
- 3 cr
- Local portion
- 3 cr
- Mode of delivery
- Contact learning
- Unit
- SeAMK Automation Engineering and Information Technology
- Campus
- SeAMK Seinäjoki, Frami
- Teaching languages
- Finnish
- Degree programmes
- Bachelor of Engineering, Automation Engineering
- Teachers
- Pasi Mikkonen
- Groups
-
AUTE22SADegree Programme in Automation Engineering, Full-time studies
- Course
- A800DB96
Evaluation scale
1-5
Objective
Students will be competent in using the mathematical methods described in the course contents to solve practical mathematical problems.
Content
Basics of machine learning:
Minimization by gradient descent
Linear regression
Logistic regression
Neural networks
Materials
Lecture material and demonstrations
Teaching methods
Lectures, exercises
Student workload
81h
Assessment criteria, satisfactory (1)
satisfactory (1-2): The student knows and understands to a satisfactory extent the basic concepts and methods discussed in the course, and is able to apply them to usual problems.
Assessment criteria, good (3)
good (3-4): The student is familiar with the concepts and methods discussed in the course, and is able to apply them to different types of problems. The student is able to combine the accumulated knowledge and skills with previous experiences in the subject.
Assessment criteria, excellent (5)
excellent (5): The student is familiar with the concepts and methods discussed in the course, and is able to apply them to a variety of different problems. The student has demonstrated creativity and innovation, and is able to find new meanings when applying what they have learned
Qualifications
Algebra and geometry, Vectors and matrices, Differential and integral calculus, Automation technology mathematics