Difference between revisions of "The Art of Scientific Research"

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# Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges by M.M. Bronstein, J. Bruna, T. Cohen, P. Veličković, 2021. [https://arxiv.org/abs/2104.13478 arxiv]
 
# Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges by M.M. Bronstein, J. Bruna, T. Cohen, P. Veličković, 2021. [https://arxiv.org/abs/2104.13478 arxiv]
 
# Deep Learning: Foundations and Concepts by C.M. Bishop, H. Bishop, 2024 [https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf version'06]
 
# Deep Learning: Foundations and Concepts by C.M. Bishop, H. Bishop, 2024 [https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf version'06]
 +
# Mathematics for Physicists: Introductory Concepts and Methods by  A. Altland. J. von Delf, 2017 [https://klassfeldtheorie.wordpress.com/wp-content/uploads/2018/10/mathematische-methoden-310117.pdf pdf]
 +
# Mathematics for Machine Learning by M.P. Deisenroth, A.A. Faisal, C.S. Ong [https://mml-book.github.io/book/mml-book.pdf pdf]
  
 
==Cath-up==
 
==Cath-up==

Revision as of 20:58, 13 August 2024

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

Check and develop your typing skills

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

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