Applied Machine Learning (3/10 weeks)
Tree ensembles, introduction to neural networks, dimension reduction and clustering
Applied Deep Learning (5/10 weeks)
Image generation, transformers and large language model pre-training, reinforcement learning (value-based methods and policy gradients), post-training and reasoning models
Computational Text Analysis and Large Language Models (4/10 weeks)
Mathematics and statistics review, introduction to neural networks, fundamentals of text embeddings, large language models
Computational Methods for PhD Students
I 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 introduction to large language models in economics research, web scraping and APIs, local and cloud databases, cloud computing, project and code organisation. All topics are illustrated with code examples and exercises in Jupyter notebooks.
To obtain a more detailed outline, please get in touch.