Mathematics 3Laajuus (5 cr)
Code: 4_EXX8130
Credits
5 op
Learning objectives
The student can solve the most common differential equations and is able to apply them in engineering applications. The student is able to use classical probability and the most common distributions to model random events. He/she knows the basics of statistical inference and hypothesis testing.
Content
- Differential equations: separable equation, first order linear equation, second order linear equation
- Combinatorics and probability
- Random variable and distribution
- Discrete and continuous distribution
- Parameter estimation and statistical tests
Materials
Croft, Davison, Hargreaves: Engineering Mathematics
Qualifications
Mathematics 1, Mathematics 2
Enrollment
01.08.2024 - 15.09.2024
Timing
01.09.2024 - 31.12.2024
Credits
5 op
Mode of delivery
Contact teaching
Teaching languages
- English
Degree programmes
- Degree Programme in Internet of Things
Teachers
- Tuukka Heiskanen
Student groups
-
IT23SPInformation Technology IOT
Teaching methods
Lectures, guided and independent exercises. The course will be split in two main sections of differential equations and probability. Each section will have their own required exam at the end of theory lectures and guided exercise sessions. In addition, students are expected to study further exercises independently.
Student workload
Lectures 56 h
Independent learning approx. 80 h
Qualifications
Mathematics 1, Mathematics 2
Materials
Engineering mathematics by John Bird
Enrollment
01.08.2024 - 15.09.2024
Timing
30.08.2024 - 31.12.2024
Credits
5 op
Mode of delivery
Contact teaching
Teaching languages
- English
Degree programmes
- Degree Programme in Industrial Management
Teachers
- Olli Sankilampi
Student groups
-
IM23SPIndustrial Management
Teaching methods
Course consists of lectures, guided and independent exercises. The course will be split in two main sections of differential equations and probability. Each section will have their own required exam at the end of theory lectures and guided exercise sessions. Passing the course requires a sufficient score from both exams as well as calculating the pre-determined number of exercises. In addition, students are expected to study further exercises independently.
Student workload
Lectures 52 h
Exams 4 h
Independent learning approximately 80 h
Qualifications
Mathematics 1, Mathematics 2
Materials
Engineering mathematics by John Bird
Enrollment
01.08.2024 - 15.09.2024
Timing
01.08.2024 - 31.12.2024
Credits
5 op
Mode of delivery
Contact teaching
Teaching languages
- English
Degree programmes
- Degree Programme in Mechanical Engineering
Teachers
- Arto Heitto
Student groups
-
IE23SPMechanical Engineering
Teaching methods
Lectures, guided and independent exercises
Student workload
Lectures 56 h
Independent learning approx. 80 h
Qualifications
Mathematics 1, Mathematics 2
Materials
Engineering mathematics by John Bird
Enrollment
01.08.2023 - 15.09.2023
Timing
04.09.2023 - 31.12.2023
Credits
5 op
Mode of delivery
Contact teaching
Teaching languages
- English
Teachers
- Tuukka Heiskanen
Responsible person
Tuukka Heiskanen
Student groups
-
EF22SPBachelor Degree Programme in Information Technology IOT
Teaching methods
Lectures, guided and independent exercises
Student workload
Lectures 56 h
Independent learning approx. 80 h
Qualifications
Mathematics 1, Mathematics 2
Materials
Engineering mathematics by John Bird
Enrollment
01.08.2023 - 15.09.2023
Timing
04.09.2023 - 22.12.2023
Credits
5 op
Mode of delivery
Contact teaching
Teaching languages
- English
Seats
0 - 50
Teachers
- Tuukka Heiskanen
Responsible person
Tuukka Heiskanen
Student groups
-
EI22SPMechanical Engineering
Teaching methods
Lectures, guided and independent exercises
Student workload
Lectures 56 h
Independent learning approx. 80 h
Qualifications
Mathematics 1, Mathematics 2
Materials
Engineering mathematics by John Bird