Applied Artificial Intelligence (AI)Laajuus (5 cr)

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.