## Week01: LLM Review
What are LLMs, how is the magic happening. A non-technical brief intro. How to work with LLMs? Plus ideas on applications. Includes suggested readings, podcasts, and vids to listen to.
[Content](week01/)
**Which AI?** See [my take on current models](week01/assets/which-ai.qmd). As of *May 2025*.
## Week02: Data and code discovery and documentation with AI
Learn how to write a clear and professional code and data documentation. LLMs are great help once you know the basics.
Case study: World Values Survey. Data is at [WVS](data/VWS)
[Content](week02/)
## Week 03: Writing Reports
You have your data and task, and need to write a short report. We compare different options with LLM, from one-shot prompt to iteration.
Case study: World Values Survey. Data is at [WVS](data/VWS)
[Content](week03/)
## Week04: Data wrangling, joining tables
When asked about what I shall add to my textbook, David Card, the Nobel winning empirical economist told me that I shall spend time with joining tables. Here we go.
Case study: simulated Austrian hotels. Data is at [hotels](data/austria-hotels)
[Content](week04/)
## Week05: Text as data 1 -- intro lecture
No course of mine can escape football (soccer). Here we look at post-game interviews to learn basics of text analysis and apply LLMs in what they are best - context dependent learning. Two class series. First is more intro to natural language processing.
Case study: football post-game interviews. Data is at [interviews](data/interviews)
[Content](week05/)
## Week06: Text as data 2 -- practice
Second class, now we are in action. How does LLM compare to humans?
Case study: football post-game interviews. Data is at [interviews](data/interviews)
[Content](week06/)
