Mathematics 3 (5cr)

Objectives (course unit)

The student
- has basic knowledge of mathematics related to data-analysis
- is able to construct graphical presentation for statistical data.
- is able to calculate basic statistical measures with computer.
- can interpret data using statistical measures and graphs.
- is able to use and apply classical data analysis for solving technical problems
- is familiar with basics of clustering and decision trees

Content (course unit)

• Classical Data Analysis
• Decision Trees, Random Forests
• Clustering, K-means
• Non-linear Optimization
• Regression: Generalized Linear Regression, Classification, Logistic Regression
• Neural Network

Assessment criteria, satisfactory (1-2) (course unit)

Understanding of basic concepts related to course topics. Can use classical data analysis and linear regression in technical problems. Capability to construct basic graphical presentations for statistical data and calculate basic statistical measures with computer. Capability to calculate exercises that are similar to discussed examples.

Assessment criteria, good (3-4) (course unit)

In addition, understanding of advanced and most concepts related to course topics. Ability to apply them in basic technical problems.

Assessment criteria, excellent (5) (course unit)

In addition, ability to apply course topics in advanced technical problems.

Location and time

Follow the TAMK intranet and course schedule from there. There will be also weekly schedule in the course Moodle.

Exam schedules

The assessment is based on witten exam and personal exercises which both are mandatory. Also keeping in the schedules is one part of the assessment. More detailed instructions will be given in classes and in those exercise instructions instructions. Writtens exam: either 20.04.2021 or 27.04.2021. Follow Moodle for that. Retest 1: 25.5.2021. Retest 2: 24.8.2021.

Assessment methods and criteria

The final grade is based on written exams and personal exercises. If you personally carry out four (4) of the learning tasks you can get the grade 1. No examination is done in this phase if you are satisfied. After that there is an examination through which the grade can be increased up to 5 depending on the success and your theoretical understanding. The course can’t be passed only by succeeding in the final exam.

Assessment scale

0-5

Teaching methods

The course will be hold in online (spring 2021) learning. Examples, demonstrations and workshops using the Zoom environment. Explainer and instructive videos in Moodle environment.

Learning materials

Most of material is in external links and some in explainer videos mainly published in Youtube. Some of those videos are public and some are hidden. There is also additional material in the course Moodle. That is in the form of additional videos, internet links, example files and links to written material.

Student workload

Total 135 hours. Self-promoted working is about 90-97 hours. Lectures, workshops, practicals, exercises and contact online lessons alltogether is about 38-45 hours.

Further information

Please follow Moodle instructions and the studying material there.

Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)

Personal learning tasks are not returned within the given time limits. Or the returned tasks do not meet the given minimum requirements with the correctness of the solutions and presentation style.

Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)

"Introductory knowledge": Personal learnings tasks accepted and elementary skills in final exam. The student can take responsibility of his own studying and needs some support from his team mates. The student follows the project schedulings with minor changes.

Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)

"Basic knowledge": Personal learnings tasks accepted and accepted basic skills in final exam. The student can do independently the practicals and exercises and can also support his group and team mates.

Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)

"Advanced knowledge": Personal learnings tasks accepted and extensive skills in final exam. The student can broadly and independently apply and understand the course topics. The student is very self motivated in solving exercises and problems in his application area and he can take responsibility of his group and team mates. The student follows exactly the course timetables and is faithful to agreed schedulings.