Step 1

The seminar

1. The warm-up 3-minute test
2. Model, Algorithm, Method: Machine learning in a nut-shell
3. Step 0 homework results discussion
4. Step 1 homework, how to read (how to search is a separate topic)
5. Structure of the main message
6. Structure of the abstract
7. Extracting keywords
8. Highlights: compressing the paper
9. Instastructure for your homework
   • GitHub: organize the repository
   • LaTeX: compile your file and commit without temporary files
10. The papers to select from
11. Optional GPT-role discussion

Resources

Step 1 Youtube video (expected with online version)

Homework

1. Set up your GitHub repository using the template
2. Select a paper to read from the list below
3. Write your own
   1. Abstract
   2. Keywords
   3. Highlights
   4. Short motivation for why you selected this paper (no templates here it is an extra topic to discuss)
4. Compile and upload TEX and PDF to GitHub (no temporary files, please)
5. Linear models. Read pages XX from the book XX, for the next warm-up test

Papers to read

Can I select any paper from the internet by my own choice? – Yes. Here are the formal requirements.

1. A clear message in the area of Theoretical Foundations of Machine Learning.
2. Top peer review journals, no ArXiv, better avoid conferences.
3. No overviews, it is another genre.
4. No Kaggle-style papers with messages like "It works, but nobody knows how".
5. No papers from another field: Linguistics, Medicine, Finance, Physics, etc. There must be only one primary subject: Machine Learning.

Transcript of the video

Appears after the seminar.