Difference between revisions of "Week 0"

From My first scientific paper
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==Resources==
 
==Resources==
 
# Introduction [http://www.machinelearning.ru/wiki/images/c/c9/M1p_lect0.pdf for students]
 
# Introduction [http://www.machinelearning.ru/wiki/images/c/c9/M1p_lect0.pdf for students]
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# Youtube [https://www.youtube.com/watch?v=vRUYqnas5fo video]
 
# Introduction [http://www.machinelearning.ru/wiki/images/3/3a/M1p_lect0_colleagues.pdf for colleagues]
 
# Introduction [http://www.machinelearning.ru/wiki/images/3/3a/M1p_lect0_colleagues.pdf for colleagues]
 
# Introduction [http://www.machinelearning.ru/wiki/images/4/40/M1p_lect0_committee.pdf for the committee]
 
# Introduction [http://www.machinelearning.ru/wiki/images/4/40/M1p_lect0_committee.pdf for the committee]

Latest revision as of 00:18, 14 April 2021

This course produces student research papers. It gathers research teams in a society. Each team combines a student, a consultant and an expert. The student is a project driver, who wants to plunge into scientific research activities. The consultant, a graduated student, conducts the research and helps the student. The expert, a professor, states the problem and enlightens the road to the goal.

  • A student is willing to learn to formally state research problems, find adequate references, generate novel and significant ideas for problem solving.
  • An advisor helps the student with technical issues, consults the student on topics of machine learning, promptly reacts to arising problems, performs evaluations and grading. Each advisor is supposed to possess sufficient publishing experience. Ideally, the advisor is writing paper on the adjacent topic. It is recommended to organize weekly reviewing process in such way that a student would input the corrections himself.
  • An expert guarantees novelty and importance of the paper, suggests the problems, provides data.

Resources

  1. Introduction for students
  2. Youtube video
  3. Introduction for colleagues
  4. Introduction for the committee

Student prerequisites

  1. Discrete Analysis and Set Theory
  2. Calculus and Mathematical Analysis
  3. Algebra, Group theory
  4. General Physics is highly welcome!
  5. Probability and Statistics
  6. Functional analysis is welcome

References to catch-up

  1. Graph Theory by Reinhard Diestel, 2017
  2. Lectures on Discrete Geometry by Jiří Matoušek, 2002
  3. Thomas’ Calculus, based on the original work by George B. Thomas, Jr, 2010
  4. Linear algebra by Jörg Liesen, Volker Mehrmann, 2015
  5. Linear algebra by Jim Hefferon, 2017
  6. Lie Groups, Lie Algebras, and Representations: An Elementary Introduction by Brian Hall, 2015
  7. Mathematics for Physicists: Introductory Concepts and Methods by Alexander Altland and Jan von Delft, 2014
  8. Mathematical Methods for Physicists by Danilo Babusci, Giuseppe Dattoli, Silvia Licciardi and Elio Sabia, 2020
  9. A First Course in Probability by Sheldon Ross, 2019
  10. Probability Theory by Alexandr A. Borovkov, 2009
  11. Lectures on Probability Theory and Mathematical Statistics by Marco Taboga, 2012
  12. Lectures on Probability Theory and Statistics by Boris Tsirelson and Wendelin Werner, 2002
  13. Elements of Information Theory by Thomas M. Cover and Joy A. Thomas, 2006