Instructor: Máté Ákos
Email: aakos.mate at gmail dot com
The syllabus (in Hungarian) is here
Week | Date | Topic | Slides | Scripts |
---|---|---|---|---|
1 | 09/13 | Fundamentals | Link | - |
2 | 09/20 | Intro to R | - | Link |
3 | 09/27 | Descriptive statistics (and pre-processing) | Link | Link |
4 | 10/04 | Descriptive statistics | Link | Link |
5 | 10/11 | Dictionary based methods | Link | Link |
6 | 10/18 | Machine learning applications | Link | Link |
7 | 10/25 | Text similarity and clustering | Link | Link |
8 | 11/01 | Scaling | Link | Link |
9 | 11/08 | Topic models | Link | TBA |
10 | 11/15 | Project proposal presentations |
The course development hugely profitted from the public resources by Pablo Barberá and Ken Benoit's LSE Course and Martijn Schoonvelde's CEU course.