Syllabus
The teaching is conducted through demonstrations of Artificial Intelligence methods on illustrative solutions of intentionally diverse practical tasks. These tasks include automatic authorship recognition, native language identification, text age estimation, predicting the success of advertising campaigns, analyzing texts from social media, conducting shopping cart analysis, analyzing and visualizing citation networks, visualizing image similarities, and various problems in psychometrics. Students are guided towards independent analysis of data sources from the humanities or social sciences and they acquire the knowledge necessary to use Artificial Intelligence methods implemented in the R software system. We particularly focus on the following topics:
Part I - Introduction to Artificial Intelligence methods
General technological principles of Artificial Intelligence and statistical Machine Learning
Historical overview of Artificial Intelligence development from a technological and user perspective
Statistical data analysis
Technologies available for processing textual data
Tools from the tidyverse package in the R software system
Part II - Traditional methods of statistical machine learning
Principles of learning from examples, classification and regression
Use and parameterization of selected learning algorithms
Clustering
Experiment evaluation
Part III - Deep Learning in Neural Networks
Neural Network Architecture
Representation of textual data using embeddings
Training Neural Networks
Annotation
Artificial Intelligence is a highly topical and growing trend penetrating into various areas of life and most scientific fields, including the humanities and social sciences. This course is a response to the increasing importance of rapidly advancing computer technologies, and it presents the technological foundations of Artificial Intelligence in an understandable way. The course is primarily designed for students in the humanities and social sciences at any level
(BSc/MSc/PhD).