Study Guide

Objective

The student knows and can apply the primary machine learning algorithms.

Content

The most common machine learning algorithms and their applications:
- Linear regression algorithms
- Non-linear regression algorithms
- Decision trees
- Naive Bayes
- Support vector machines
- K-nearest neighbors
- K-means
- Random forest
- Dimensionality reduction

Artificial neural networks

Location and time

Rovaniemi campus, Jokiväylä 11, Rovaniemi.

Tentatively, one four-hour meeting per week, on the weeks 36-48 excluding the week 42.

Materials

The materials shall be put to the Moodle-workspace for the course unit.

Teaching methods

Lectures, exercises, examination.

Exam schedules

The examination dates shall be agreed in the beginning of the course unit.

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The students knows the most common machine learning algorithms and their applications.

Assessment criteria, good (3)

The students knows the most common machine learning algorithms and can apply some of them to the given tasks.

Assessment criteria, excellent (5)

The student can apply a variety of machine learning algorithms and compare their efficiency and feasibility to the given tasks.

Assessment methods and criteria

Exercises, examination.