Machine LearningLaajuus (5 cr)
Code: AT00BY43
Credits
5 op
Objective
The student is able to
- take advantage of both supervised and unsupervised machine learning in an appropriate way
- implement the fitting of the machine learning model
- take advantage of the supply of cloud services
- take into account the ethical guidelines of the authorities and the technology industry
- make use of existing machine learning ecosystems and equipment
Enrollment
06.05.2024 - 30.08.2024
Timing
04.11.2024 - 13.12.2024
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance learning
Unit
Faculty of Technology (LAB)
Campus
E-campus, Lahti
Teaching languages
- Finnish
Seats
10 - 40
Degree programmes
- Bachelor’s Degree Programme in Environmental Engineering
- Bachelor's Degree Programme in Information Technology
Teachers
- Matti Welin
- Minna Asplund
- Rami Viksilä
Scheduling groups
- Luennot 1 (Size: 100. Open UAS: 0.)
Groups
-
TLTITVT21SV
-
TLTITVT21K
-
TLTIENTEC23KM
-
TLTIENTEC22KM
Small groups
- Lecture 1
Learning outcomes
The student is able to
- take advantage of both supervised and unsupervised machine learning in an appropriate way
- implement the fitting of the machine learning model
- take advantage of the supply of cloud services
- take into account the ethical guidelines of the authorities and the technology industry
- make use of existing machine learning ecosystems and equipment
Assessment scale
1-5
Enrollment
20.11.2023 - 05.01.2024
Timing
11.03.2024 - 26.04.2024
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance learning
Unit
Faculty of Technology (LAB)
Campus
E-campus, Lahti
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Information Technology
- Complementary competence and optional courses, Bachelors
Teachers
- Matti Welin
- Minna Asplund
- Rami Viksilä
Scheduling groups
- Verkkoluento 1 (Size: 500. Open UAS: 0.)
Groups
-
TLTITVT22K
-
TLTITVT22SV
-
TLTITVT23KM
Small groups
- Online lecture 1
Learning outcomes
The student is able to
- take advantage of both supervised and unsupervised machine learning in an appropriate way
- implement the fitting of the machine learning model
- take advantage of the supply of cloud services
- take into account the ethical guidelines of the authorities and the technology industry
- make use of existing machine learning ecosystems and equipment
Assessment scale
1-5
Enrollment
15.05.2023 - 01.09.2023
Timing
06.11.2023 - 15.12.2023
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance learning
Unit
Faculty of Technology (LAB)
Campus
E-campus, Lahti
Teaching languages
- Finnish
Seats
10 - 40
Degree programmes
- Bachelor's Degree Programme in Information Technology
Teachers
- Matti Welin
- Minna Asplund
- Rami Viksilä
Scheduling groups
- Verkkoluento 1 (Size: 0. Open UAS: 0.)
Groups
-
TLTITVT21K
-
TLTITVT20SV
Small groups
- Verkkoluento 1
Learning outcomes
The student is able to
- take advantage of both supervised and unsupervised machine learning in an appropriate way
- implement the fitting of the machine learning model
- take advantage of the supply of cloud services
- take into account the ethical guidelines of the authorities and the technology industry
- make use of existing machine learning ecosystems and equipment
Assessment scale
1-5
Enrollment
21.11.2022 - 08.01.2023
Timing
13.03.2023 - 28.04.2023
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance learning
Unit
Faculty of Technology (LAB)
Campus
E-campus, Lahti
Teaching languages
- Finnish
Seats
10 - 40
Degree programmes
- Bachelor's Degree Programme in Information Technology
Teachers
- Matti Welin
- Minna Asplund
- Rami Viksilä
Scheduling groups
- Verkkoluento 1 (Size: 500. Open UAS: 0.)
Groups
-
TLTITVT21SV
-
TLTITVT22K
-
TLTITVT21K
-
07TVT20K
-
TLTITVT22SV
-
TLTITVT20SV
Small groups
- Verkkoluento 1
Learning outcomes
The student is able to
- take advantage of both supervised and unsupervised machine learning in an appropriate way
- implement the fitting of the machine learning model
- take advantage of the supply of cloud services
- take into account the ethical guidelines of the authorities and the technology industry
- make use of existing machine learning ecosystems and equipment
Assessment scale
1-5
Enrollment
01.07.2022 - 01.09.2022
Timing
07.11.2022 - 16.12.2022
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance learning
Unit
Faculty of Technology (LAB)
Campus
E-campus, Lahti
Teaching languages
- Finnish
Seats
10 - 40
Degree programmes
- Bachelor's Degree Programme in Information Technology
Teachers
- Matti Welin
- Minna Asplund
- Rami Viksilä
Scheduling groups
- Verkkoluento 1 (Size: 0. Open UAS: 0.)
Groups
-
07TVT20K
-
TLTITVT20SV
Small groups
- Verkkoluento 1
Learning outcomes
The student is able to
- take advantage of both supervised and unsupervised machine learning in an appropriate way
- implement the fitting of the machine learning model
- take advantage of the supply of cloud services
- take into account the ethical guidelines of the authorities and the technology industry
- make use of existing machine learning ecosystems and equipment
Assessment scale
1-5
Enrollment
19.11.2021 - 09.01.2022
Timing
21.03.2022 - 29.04.2022
Number of ECTS credits allocated
5 op
Virtual portion
3 op
Mode of delivery
40 % Contact teaching, 60 % Distance learning
Unit
Faculty of Technology (LAB)
Campus
Teaching languages
- Finnish
Seats
10 - 30
Degree programmes
- Bachelor's Degree Programme in Information Technology
Teachers
- Matti Welin
- Minna Asplund
- Juhani Grape
- Rami Viksilä
Scheduling groups
- Opetus (Size: 0. Open UAS: 0.)
Groups
-
07TVT20K
-
07TVT19SV, Lahti
Small groups
- Lectures
Learning outcomes
The student is able to
- take advantage of both supervised and unsupervised machine learning in an appropriate way
- implement the fitting of the machine learning model
- take advantage of the supply of cloud services
- take into account the ethical guidelines of the authorities and the technology industry
- make use of existing machine learning ecosystems and equipment
Assessment scale
1-5