Week 1

From My first scientific paper
Revision as of 00:05, 18 February 2021 by Wiki (talk | contribs) (→‎Resources)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

Set the toolbox

  1. Editors. Install LaTeX: MikTeX for Windown, TeX Live for Linux, and for Mac OS. Sign up V2 OverLeaf ShareLaTeX.
  2. Install the editor TeXnic Center or its alternative WinEdt for Windows, TeXworks for Linux, and TeXmakerfor Mac OS.
  3. Editors. Install LaTeX: MikTeX for Windown, TeX Live for Linux, and for Mac OS. Sign up V2 OverLeaf ShareLaTeX.
  4. Install the editor TeXnic Center or its alternative WinEdt for Windows, TeXworks for Linux, and TeXmakerfor Mac OS.
  5. Download the paper template, ZIP and compile it.
  6. References. Read BibTeX.
  7. Install bibliographic collection software JabRef (can be postponed).
  8. Communications. Sign up GitHub.
    • Important: address and login like Name.Surname or Name-Surname (it depends on system conventions) is welcome.
    • Introductory sliders on Version Control System.
    • Introduction to GitHub.
    • The first steps in GitHub.
  9. Download a shell: Desktop.GitHub, or use a command line to synchronise your project.
  10. Sign up MachineLearning.ru. Send your login name to your coordinator or to mlalgorithms [at] gmail [dot] com.
  11. Create your page example.
  12. Programming. Install Python Anaconda, PyCharm (alternative Visual Studio), Notebook online Google.Colab.
  13. Add. As alternative install and try Matlab (MIPT provides free version), (alternative Octave), R-project, Wofram Mathematica.
  14. Add. Read with pleasure Кутателадзе С. С. Советы эпизодическому переводчику and Сосинский А. Б. Как написать математическую статью по-английски (Ru).

Express your project-view

  • Write four lines: a goal and motivation (what and why?) for any project you believe worth noting. Refer to Projects.
  • If you have subscribe to this course with your own project: please connect your advisor or consultant and write your project description as it shown in the project description template.
  • Go to Stepik:
    1. Sign up to the stepik.org.
    2. Run the course m1p.
    3. Put your project description to Step 1.1.
    4. Participate to the peer-review.

Resources

References to catch up

  1. A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018
  2. Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David, 2014
  3. Mathematics for Machine learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
  4. Mathematics for Physicists: Introductory Concepts and Methods by Alexander Altland & Jan von Delf
  5. Python notes for professionals by GoalKicker.com Free Programming Books.
  6. Лагутин М.Б. Наглядная математическая статистика, М.: Бином, 2009. См. также вырезку (Ru)
  7. Bishop C.P. Pattern recognition and machine learning, Berlin: Springer, 2008
  8. MackKay D. Information Theory, Pattern Recognition and Neural Networks, Inference.org.uk, 2009