Introduction to Machine Learning Methods (5cr)

Evaluation scale

H-5

Content scheduling

- Theory and practice of basic ML models and methods for typical tasks encountered in unsupervised and supervised learning
- Common traditional ML algorithms
- Preprocessing data for ML algorithms
- Error metrics and ML model performance evaluation
- Use of suitable tools (e.g., a high-level ML application programming interface, like scikit-learn) for building solutions

Objective

- Knowledge and skills to understand basic machine learning (ML) models and methods, and to choose and apply them in a principled and sound way in basic tasks
- Abilities for computational thinking that utilizes machine learning, for problem solving

Content

- Theory and practice of basic ML models and methods for typical tasks encountered in at least unsupervised and supervised learning
- Use of suitable tools (e.g., a high-level ML application programming interface) for building solutions

Location and time

The course will be studied on-campus. Lapland University of Applied Sciences, Rantavitikka Campus, 12.1.2026 - 15.5.2026.

Materials

The study material of the course will be in the course's Moodle workspace. Additional material can be searched from the internet as needed. It's also recommended to independently search for more information during the course.

Teaching methods

Lectures, workshops, examples, exercises and self-supervised work.

The Arene's AI recommendations and traffic light model for universities of applied sciences will be applied during the course. Whether AI is allowed during an exercise, will depend on the exercise itself.

Employer connections

The study unit has been connected to the semester project held during the same semester, which will have common technologies used in the working life. The technologies and related examples during the course will be partially based on the needs of the semester project.

Exam schedules

The course will be graded based on personal work and exercises.

International connections

The study unit will be in English.

Completion alternatives

Alternative ways to study the course can be inquired from the instructor.

Student workload

The study unit is 5 ECTS, which amounts to 135 hours of work in total (approximately). ON average, your work will be distributed the following way:

- Lectures and workshops: 50 h
- Self-study and preparation: 45 h
- Graded tasks: 40h

Assessment criteria, satisfactory (1)

Grade 1: The student knows the theory on the considered ML models and methods. The student is able to solve basic ML problems, using the considered tools.

Assessment criteria, good (3)

Grade 3: The student understands the theory on the considered ML models and methods. The student is able to solve a variety of basic ML problems, using the considered tools, suitably.

Assessment criteria, excellent (5)

Grade 5: The student understands the theory on the considered ML models and methods. The student is able to solve a variety of basic ML problems, using the considered tools, most suitably.