MY474 Applied Machine Learning for Social Science
MY475 Applied Deep Learning for Social Science
MY459 Computational Text Analysis and Large Language Models
Computational Methods for PhD Students
I also occasionally teach a week-long course (or shorter workshops on individual topics) for PhD Economics students as guest teacher at other departments. Analogously to already common pre-sessional courses in mathematics and statistics, the idea of this course is to cover broad fundamentals in applied computational methods for research.
Topics: Version control with Git and GitHub, Python fundamentals up to classes and inheritance, algorithmic complexity, tabular data processing, visualisation, textual data and natural language processing (with a focus on large language models and applications to economics research), web scraping, obtaining data from web APIs, local and cloud databases, cloud computing, project and code organisation. All topics are illustrated through code examples and exercises in Jupyter notebooks. An optional day can be added to discuss fundamentals in statistical machine learning and deep learning with further code examples in Python.
To obtain a more detailed outline, please get in touch.