The Art of Scientific Research

The Art of Scientific Research

This is a preparatory course for the main part of m1p.

The student's response-based syllabus

1. We start
2. Prepare your tools
3. Check the foundations
4. How to measure impact?
5. Describe your system
6. Write the abstract
7. Write the intro
8. Review the paper
9. Deliver a message
10. Your one-slide talk
11. Blind management game
12. List your ideas
13. List the foundations
14. Suggest an impactful theorem
15. Review for your topic
16. Good, bad, ugly: tell the difference
17. Tell about a scientific society
18. Reproducible computational experiment
19. Computer supported brainstorming
20. Conferences and journals, review and schedules
21. Writing a grant proposal

Scoring

1. Tests at the beginning of a seminar
2. Talks at the end of a seminar
3. Downloads of the homework
4. The coursework

Similar courses

1. Around

Main references

1. (long reading 2196 pages) Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning by Jean Gallier and Jocelyn Quaintance, 2024. pdf, github
2. (fun reading) The Art of Scientific Investigation by W. I. B. Beveridge, 1957 pdf
3. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems. and Control by S.L. Brunton and J. N. Kutz, 2019.
4. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges by M.M. Bronstein, J. Bruna, T. Cohen, P. Veličković, 2021. arxiv
5. Deep Learning: Foundations and Concepts by C.M. Bishop, H. Bishop, 2024 version'06
6. Mathematics for Physicists: Introductory Concepts and Methods by A. Altland. J. von Delf, 2017 pdf
7. Mathematics for Machine Learning by M.P. Deisenroth, A.A. Faisal, C.S. Ong pdf

Cath-up

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Dates

Sat 9:30 – 10:50 zoom | Sept 7 14 21 28 | Now 5 12 19 26 | Oct 2 9 16 23 30 | Dec 7 14 21 28