Applied Artificial Intelligence (AI)Laajuus (5 cr)
Course unit code: ICB013AS3AE
General information
- ECTS credits
- 5 cr
- Teaching language
- English
Learning objectives
The overall learning objective of the course is to give the students insight into machine learning and natural language processing (NLP) technologies and their application in practice.
Upon successful completion of the course, the student:
• knows the main concepts of machine learning and natural language processing,
• can apply a machine learning method in a business case,
• can build a simple chatbot,
• has gained basic skills in using selected machine learning and NLP tools and
• is capable of planning and implementing a project involving AI technologies
Contents
• Main concepts of machine learning and natural language processing.
• Business cases where machine learning and natural language processing methods are used.
• Tools (e.g. Anaconda, Python library scikit-learn ) for machine learning.
• Tools (e.g. Google Dialogflow, IBM Watson Virtual Assistant)
• Course project involving AI technologies.
Implementation methods, demonstration and Work&Study
Implementation Methods:
- Virtual, blended and contact implementations
- Recognition of Prior Learning
Assessment criteria - grade 1
When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25 %, your grade will be lowered by one. If you are absent more than 50 %, the course is failed.
Assessment criteria, approved/failed
When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25 %, your grade will be lowered by one. If you are absent more than 50 %, the course is failed.