Week 1

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.
   • Read LaTeX on MachineLearning (Ru).
   • Useful: Wikibooks LaTeX, К.В.Воронцов. LaTeX2e в примерах (Ru).
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.
   • Read LaTeX on MachineLearning (Ru).
   • Useful: Wikibooks LaTeX, К.В.Воронцов. LaTeX2e в примерах.
   • Read Львовский С.М. Набор и верстка в системе LaTeX (Ru).
   • Read Мильчин А.Э. Чельцова Л.К. Справочник издателя и автора (Ru).
5. Download the paper template, ZIP and compile it.
6. References. Read BibTeX.
   • An example of a bibliographic database.
   • An example of a bibliographic record.
   • An example of draft review LinkReview.
   • List of databases and search engines.
   • List of data-sets for Machine Learning projects.
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.
    • Development for ML: PyTorch
    • Style formatting: Codestyle pep8
13. Add. As alternative install and try Matlab (MIPT provides free version), (alternative Octave), R-project, Wofram Mathematica.
    • Read Introduction to Matlab].
    • Read Matlab code style, reporting and documenting].
    • Read Matlab Programming Style Guidelines.
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).
   • Read Львовский С.М. Набор и верстка в системе LaTeX (Ru).
   • Read Мильчин А.Э. Чельцова Л.К. Справочник издателя и автора (Ru).
9. Download the paper template, ZIP and compile it.
10. References. Read BibTeX.
    • An example of a bibliographic database.
    • An example of a bibliographic record.
    • An example of draft review LinkReview.
    • List of databases and search engines.
    • List of data-sets for Machine Learning projects.
11. Install bibliographic collection software JabRef (can be postponed).
12. 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.
13. Download a shell: Desktop.GitHub, or use a command line to synchronise your project.
14. Sign up MachineLearning.ru. Send your login name to your coordinator or to mlalgorithms [at] gmail [dot] com.
15. Create your page example.
16. Programming. Install Python Anaconda, PyCharm (alternative Visual Studio), Notebook online Google.Colab.
    • Development for ML: PyTorch
    • Style formatting: Codestyle pep8
17. Add. As alternative install and try Matlab (MIPT provides free version), (alternative Octave), R-project, Wofram Mathematica.
    • Read Introduction to Matlab].
    • Read Matlab code style, reporting and documenting].
    • Read Matlab Programming Style Guidelines.
18. 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