Introduction to Artificial IntelligenceLaajuus (3 cr)
Code: TT00EV75
Credits
3 op
Teaching language
- English
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2024 - 31.07.2024
Timing
01.08.2024 - 31.07.2025
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2024 - 31.07.2024
Timing
01.08.2024 - 31.07.2025
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
0-5
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2024 - 31.07.2024
Timing
01.08.2024 - 31.07.2025
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Teacher in charge
Janne Salonen
Groups
-
VIOPE_NonStop7VIOPE_NonStop7
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2024 - 31.07.2024
Timing
01.08.2024 - 31.07.2025
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Campus
Karaportti 2
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Timing
16.04.2024 - 31.12.2025
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 5000
Degree programmes
- Information and Communication Technology
Teachers
- Virve Prami
Teacher in charge
Janne Salonen
Groups
-
VIOPE_2025_MAKSULLINEN_JARJESTELMAOpen UAS 2025
-
VIOPE_2024_MAKSULLINEN_JARJESTELMAViope TiVi (NonStop), year 2024
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
01.01.2024 - 31.12.2026
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 10000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
Verkko_opinnot_NonStop_120_opPath Studies (TiVi-NonStop), 120 ECTS
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS and CampusOnline Student via https://hakija.oma.metropolia.fi/
Metropolia's Degree Student: https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
01.01.2024 - 31.12.2027
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 10000
Degree programmes
- Degree Programme in Information Technology
Teachers
- Mika Hyyryläinen
Groups
-
IT_path_180_ectsInformation Technology, Open path 180 ECTS
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS and CampusOnline Student via https://hakija.oma.metropolia.fi/
Metropolia's Degree Student: https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
01.01.2024 - 31.12.2025
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
AVOIN_AMK_TIVI_73_opComplementary Informatics past graduate degree
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
01.01.2024 - 31.07.2025
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 5000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
- Virve Prami
Teacher in charge
Janne Salonen
Groups
-
VIOPE_2024_MAKSULLINEN_JARJESTELMAViope TiVi (NonStop), year 2024
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Enrollment
02.07.2024 - 31.07.2024
Timing
01.01.2024 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Teacher in charge
Janne Salonen
Groups
-
VIOPE_NonStop6VIOPE_NonStop6
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2024 - 31.07.2024
Timing
01.01.2024 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Teacher in charge
Janne Salonen
Groups
-
Viope_nonstop_9Viope_nonstop_9
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2024 - 31.07.2024
Timing
01.01.2024 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Teacher in charge
Janne Salonen
Groups
-
Viope_nonstop_11Viope_nonstop_11
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2024 - 31.07.2024
Timing
01.01.2024 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Teacher in charge
Janne Salonen
Groups
-
Viope_nonstop_12Viope_nonstop_12
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2024 - 31.07.2024
Timing
01.01.2024 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Teacher in charge
Janne Salonen
Groups
-
Viope_nonstop_13Viope_nonstop_13
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2023 - 31.07.2023
Timing
01.08.2023 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Campus
Karaportti 2
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2023 - 31.07.2023
Timing
01.08.2023 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Campus
Karaportti 2
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2023 - 31.07.2023
Timing
01.08.2023 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Campus
Karaportti 2
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2023 - 31.07.2023
Timing
01.08.2023 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Campus
Karaportti 2
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2023 - 31.07.2023
Timing
01.08.2023 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Campus
Karaportti 2
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.07.2023 - 31.07.2023
Timing
01.08.2023 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Campus
Karaportti 2
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.12.2022 - 31.12.2022
Timing
01.01.2023 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Campus
Karaportti 2
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Enrollment
02.12.2022 - 31.12.2022
Timing
01.01.2023 - 31.07.2024
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Campus
Karaportti 2
Teaching languages
- English
Degree programmes
- Information and Communication Technology
Teachers
- Janne Salonen
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Timing
01.08.2023 - 31.12.2023
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
- Virve Prami
Teacher in charge
Janne Salonen
Groups
-
ATX22TV_Study_PackagesOpen UAS: NonStop Study Packages
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop3VIOPE_NonStop3
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop3VIOPE_NonStop3
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Degree Programme in Information Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop3VIOPE_NonStop3
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS and CampusOnline Student via https://hakija.oma.metropolia.fi/
Metropolia's Degree Student: https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop3VIOPE_NonStop3
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop4VIOPE_NonStop4
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop4VIOPE_NonStop4
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop4VIOPE_NonStop4
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop4VIOPE_NonStop4
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Degree Programme in Information Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop4VIOPE_NonStop4
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS and CampusOnline Student via https://hakija.oma.metropolia.fi/
Metropolia's Degree Student: https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop4VIOPE_NonStop4
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop5VIOPE_NonStop5
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop5VIOPE_NonStop5
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Degree Programme in Information Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop5VIOPE_NonStop5
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS and CampusOnline Student via https://hakija.oma.metropolia.fi/
Metropolia's Degree Student: https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop5VIOPE_NonStop5
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
14.09.2022 - 31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop3VIOPE_NonStop3
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
14.09.2022 - 31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 5000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop3VIOPE_NonStop3
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
14.09.2022 - 31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop4VIOPE_NonStop4
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
14.09.2022 - 31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 5000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop4VIOPE_NonStop4
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
14.09.2022 - 31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop4VIOPE_NonStop4
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
14.09.2022 - 31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 5000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop4VIOPE_NonStop4
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
14.09.2022 - 31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop5VIOPE_NonStop5
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
14.09.2022 - 31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 5000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop5VIOPE_NonStop5
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
01.08.2022 - 31.12.2023
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
- Virve Prami
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
01.08.2022 - 31.12.2023
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
- Virve Prami
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
01.08.2022 - 31.12.2023
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
- Virve Prami
Teacher in charge
Janne Salonen
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
01.08.2022 - 31.12.2023
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
- Virve Prami
Teacher in charge
Janne Salonen
Groups
-
VIOPE_NonStop7VIOPE_NonStop7
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
01.08.2022 - 31.12.2023
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
- Virve Prami
Teacher in charge
Janne Salonen
Groups
-
VIOPE_NonStop7VIOPE_NonStop7
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
01.08.2022 - 31.12.2023
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
- Virve Prami
Teacher in charge
Janne Salonen
Groups
-
VIOPE_NonStop7VIOPE_NonStop7
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
24.05.2022 - 31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 5000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStop3VIOPE_NonStop3
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS Student via https://hakija.oma.metropolia.fi/
CampusOnline Students via eform which can be find via https://campusonline.fi
Metropolia's Degree Student
- Go to https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
If you have problems with enrolling into this course or questions about it, please contact to viopesupport@metropolia.fi
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
01.01.2022 - 31.12.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 1000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
VIOPE_NonStopViope (NonStop)
-
VIOPE_2022_MAKSULLINEN_JÄRJESTELMÄViope TiVi (NonStop), year
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS and CampusOnline Student via https://hakija.oma.metropolia.fi/
Metropolia's Degree Student: https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.
Timing
15.09.2021 - 31.12.2023
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Karaportti 2
Teaching languages
- English
Seats
0 - 5000
Degree programmes
- Information and Communication Technology
Teachers
- Mika Hyyryläinen
Groups
-
CareerBoost_TXK_21Career Boost 21 (Tivi)
-
CareerBoost_TXK_22Career Boost 22 (TiVi)
-
Career_Boost_TXK_21Career Boost 2021 (TiVi)
Objective
After completing a course, student has learned what are the basic tehniques to manifest artificial intelligence in practise.
Content
- Introduction
- Data Exploration
- Supervised learning
- Unsupervised learning
- Deep Learning
- Reinforcement learning
Location and time
Course is delivered via Metropolia's Viope environment and it can be done in own pace.
Materials
All of course material is online in Metropolia's Viope.
Teaching methods
Course is 100% online (Self-Study) course and it can be done in own pace.
Employer connections
N/A
Exam schedules
N/A
International connections
N/A
Completion alternatives
N/A
Student workload
Course is self-study course, so, timetable is totally up to student her-/himself.
Content scheduling
Course is self-study course, so, student can schedule course her-/himself.
Further information
ENROLLING
Open UAS and CampusOnline Student via https://hakija.oma.metropolia.fi/
Metropolia's Degree Student: https://vw4.viope.com/login?org=metropolia
- Register to system -> Click the link “Enroll on the course”
- Choose any course of (NonStop).
- Fill the others fields
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, approved/failed
After student has done 80% of course he/she get's grading pass.
Assessment methods and criteria
After student has done at least 80% of tasks he/she can get grade pass.