The Text Mining course is an elective course within the eXplainable Artificial Intelligence in healthcare Management (xAIM) master’s programme. As Artificial Intelligence (AI) becomes increasingly important, especially within the healthcare sector, it is becoming crucial to address the lack of digital skills training within the sector. This master’s programme seeks to address this by training qualified healthcare professionals in the field of AI and computer scientists in the field of healthcare.

Text Mining: Learning outcomes

In the Text Mining course, the student will acquire knowledge on the use of the core machine learning algorithms for text mining. With this course, students will be introduced to natural language processing, text mining, and text analysis. They will learn to accomplish various text-related data mining tasks through visual programming. After the completion, students will be able to preprocess textual data, understand specifics of text, transform raw text to attribute-value representation and evaluate language-based models.

Lecture 6: Explaining Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA) is a popular technique used to extract and identify themes or topics from unstructured textual data. In this lecture is it explained what are the assumptions of the LDA method. The lecture provides a detailed step-by-step explanation of the algorithm (based on Gibbs sampling) with an example from medicine. It provides a comparison between Gibbs sampling and variational inference. Finally, it gives an overview of tools that include LDA with additional options that span beyond the described method.

The lecture combines theory with practical examples for hands-on learning. The lesson is prepared by Ajda Pretnar Žagar and Blaž Zupan with the support of members of the Bioinformatics Lab at the University of Ljubljana in Slovenia.