Difference between revisions of "Week 1"

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(Created page with "==Set the toolbox== # '''Editors'''. Install LaTeX: [http://miktex.org MikTeX] for Windown, [http://www.tug.org/texlive/ TeX Live] for Linux, and for Mac OS. Sign up [https:/...")
 
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# Install the editor [http://www.texniccenter.org/ TeXnic Center] or its alternative [http://www.winedt.com/ WinEdt] for Windows, [http://www.tug.org/texworks/ TeXworks] for Linux, and [https://www.xm1math.net/texmaker/ TeXmaker]for Mac OS.
 
# Install the editor [http://www.texniccenter.org/ TeXnic Center] or its alternative [http://www.winedt.com/ WinEdt] for Windows, [http://www.tug.org/texworks/ TeXworks] for Linux, and [https://www.xm1math.net/texmaker/ TeXmaker]for Mac OS.
 
#* Read [http://www.machinelearning.ru/wiki/index.php?title=LaTeX LaTeX on MachineLearning] (Ru).
 
#* Read [http://www.machinelearning.ru/wiki/index.php?title=LaTeX LaTeX on MachineLearning] (Ru).
#* Useful: [https://en.wikibooks.org/wiki/LaTeX Wikibooks LaTeX], [http://www.machinelearning.ru/wiki/images/e/e4/LaTeX_examples.pdf К.В.Воронцов. LaTeX2e в примерах] (==Set the toolbox==
+
#* Useful: [https://en.wikibooks.org/wiki/LaTeX Wikibooks LaTeX], [http://www.machinelearning.ru/wiki/images/e/e4/LaTeX_examples.pdf К.В.Воронцов. LaTeX2e в примерах] (Ru).
 
# '''Editors'''. Install LaTeX: [http://miktex.org MikTeX] for Windown, [http://www.tug.org/texlive/ TeX Live] for Linux, and for Mac OS. Sign up [https://v2.overleaf.com/ V2 OverLeaf  ShareLaTeX].
 
# '''Editors'''. Install LaTeX: [http://miktex.org MikTeX] for Windown, [http://www.tug.org/texlive/ TeX Live] for Linux, and for Mac OS. Sign up [https://v2.overleaf.com/ V2 OverLeaf  ShareLaTeX].
 
# Install the editor [http://www.texniccenter.org/ TeXnic Center] or its alternative [http://www.winedt.com/ WinEdt] for Windows, [http://www.tug.org/texworks/ TeXworks] for Linux, and [https://www.xm1math.net/texmaker/ TeXmaker]for Mac OS.
 
# Install the editor [http://www.texniccenter.org/ TeXnic Center] or its alternative [http://www.winedt.com/ WinEdt] for Windows, [http://www.tug.org/texworks/ TeXworks] for Linux, and [https://www.xm1math.net/texmaker/ TeXmaker]for Mac OS.

Revision as of 18:27, 9 February 2021

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).

Resources

  1. [Slides for week 0].
  2. [Video for week 0].
  3. [Slides for week 1].
  4. [Video for week 1].
  5. Short course description.

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

).

  1. Download the paper template, ZIP and compile it.
  2. References. Read BibTeX.
  3. Install bibliographic collection software JabRef (can be postponed).
  4. 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.
  5. Download a shell: Desktop.GitHub, or use a command line to synchronise your project.
  6. Sign up MachineLearning.ru. Send your login name to your coordinator or to mlalgorithms [at] gmail [dot] com.
  7. Create your page example.
  8. Programming. Install Python Anaconda, PyCharm (alternative Visual Studio), Notebook online Google.Colab.
  9. Add. As alternative install and try Matlab (MIPT provides free version), (alternative Octave), R-project, Wofram Mathematica.
  10. Add. Read with pleasure Кутателадзе С. С. Советы эпизодическому переводчику and Сосинский А. Б. Как написать математическую статью по-английски (Ru).

Resources

  1. [Slides for week 0].
  2. [Video for week 0].
  3. [Slides for week 1].
  4. [Video for week 1].
  5. Short course description.

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