Introduction to Machine Learning Methods (5 cr)

Evaluation scale

H-5

Content scheduling

- Theory and practice of basic ML models and methods for typical tasks encountered in at least 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

Lapland University of Applied Sciences, Rantavitikka Campus, 8.1.2024 - 15.5.2024.

Materials

Lecture materials and exercises will be placed in the Moodle workspace.

Teaching methods

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

Exam schedules

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

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.