Data Handling and Machine Learning (5 cr)

Objective

After completion of the course, the student
• understands the possibilities in data handling, modelling and, particularly, machine learning
• has hands-on experience in data storage, retrieval, and manipulation as well as the methods and tools in machine learning.

Content

• Large volumes of data in ICT business: applicability, models, opportunities, and processes, legislative and ethical constraints
• Data acquisition and preprocessing
• Data management solutions
• Machine learning methods (classification, association analysis, clustering, prediction of numeric values) , their fields of use and applicability
• Machine learning software
• Validation and visualisation of results
• Machine learning in natural language processing

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

The student
• has achieved the objectives of the course to a satisfactory level
• is able to identify and define the concepts, models and in the subject area of the course
• has completed the learning tasks required for the course to the minimum standard.

Assessment criteria, good (3)

The student
• has achieved the objectives of the course well
• is able to identify, define and use the concepts, models and tools in the subject area of the course
• has completed the learning tasks of the course at a good level.

Assessment criteria, excellent (5)

The student
• has achieved the objectives of the course with excellent marks
• is able to identify, define and use and apply the concepts and models in the subject area of the course in a variety of ways
• has completed the learning tasks of the course at an excellent level and has put considerable own effort into their solutions.

Assessment criteria, approved/failed

The student
• has achieved the objectives of the course to a satisfactory level
• is able to identify and define the concepts, models and in the subject area of the course
• has completed the learning tasks required for the course to the minimum standard.