Introduction to Machine Learning MethodsLaajuus (5 cr)
Code: R504D123
Credits
5 op
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
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.
Enrollment
01.10.2024 - 12.01.2025
Timing
13.01.2025 - 18.04.2025
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 30
Teachers
- Tuomas Valtanen
Responsible person
Tuomas Valtanen
Student groups
-
R54D23SBachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2023
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, 13.1.2025 - 15.5.2025.
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.
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
Evaluation scale
H-5
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.
Assessment methods and criteria
The course will be graded on the scale of 1 - 5 and failed (0). The grading will be based on the submitted exercises/assignments.
Enrollment
02.10.2023 - 14.01.2024
Timing
15.01.2024 - 31.05.2024
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 30
Teachers
- Tuomas Valtanen
Responsible person
Tuomas Valtanen
Student groups
-
R54D22S
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.
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
Evaluation scale
H-5
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.
Assessment methods and criteria
The course will be graded on the scale of 1 - 5 and failed (0). The grading will be based on the submitted exercises/assignments.