Azure Machine Learning (15 ECTS)
Code: TT00EO92-3006
General information
- Timing
- 01.01.2023 - 31.12.2023
- Implementation has ended.
- Number of ECTS credits allocated
- 15 ECTS
- Virtual portion
- 15 ECTS
- Mode of delivery
- Online
- Campus
- Karaportti 2
- Teaching languages
- English
- Seats
- 0 - 1000
- Degree programmes
- Information and Communication Technology
- Teachers
- Virve Prami
- Course
- TT00EO92
Location and time
Course can be done with own pace in TechClass environment.
Materials
Lecture slides
Tutorial videos
Quizzes
Exercises
Project
Employer connections
N/A
Exam schedules
Online.
International connections
N/A
Completion alternatives
N/A
Evaluation methods and criteria
Exercises 50%
Quizzes 25%
Project 25%
Student workload
Lectures = 85h
Exercises = 95h
Self-study = 100h
Quizzes = 15h
Project = 65h
Total = 360 hours
Content scheduling
Up to Student her-/himself.
Teaching methods
100% online Self-Study course.
- Tutorial Videos
- Exercises
- Quiz
- Project
- Self-study
Evaluation scale
Hyväksytty/Hylätty
Assessment criteria, satisfactory (1)
- The student is familiar with the cloud-based service.
- The student is familiar with about Azure Machine Learning Studio.
- The student knows about how to use Azure Machine Learning Designer service.
- The student is familiar how to train a model using Azure Machine Learning Designer.
- The student is familiar with concept of Automated Machine Learning service in AML.
- The student is familiar with different compute resource in AML.
Assessment criteria, good (3)
- The student knows how to use Automated Machine Learning Service to implement and deploy different machine learning models.
- The student knows the how to use Jupyter notebook and run and manage it.
- The student is familiar with different assets in Azure Machine Learning Studio.
- The student is familiar with Azure Machine Learning SDK.
- The student knows how to setup his/her local computer.
- The student knows how to use Azure machine learning compute resource to train his/her models.
- The student is familiar with the concept of Experiment.
- The student is familiar with the concept of Event Grid.
Assessment criteria, excellent (5)
- The student knows how to create an environment for his/her model.
- The student can prepare his/her code for deploying different machine learning models.
- The student can understand the technical document of Azure Machine learning Services.
- The student is familiar with Microsoft cognitive service.
- The student knows how transfer learning models work.