Difference between revisions of "Week 1"

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==Resources==
 
==Resources==
 +
To be updated after class, Feb 15th
 
* [http://www.machinelearning.ru/wiki/images/c/c9/M1p_lect0.pdf Slides for week 0].
 
* [http://www.machinelearning.ru/wiki/images/c/c9/M1p_lect0.pdf Slides for week 0].
 
* [https://www.youtube.com/watch?v=vRUYqnas5fo Video for week 0].
 
* [https://www.youtube.com/watch?v=vRUYqnas5fo Video for week 0].

Revision as of 16:35, 15 February 2024

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.
    • Run GitHub Copilot
    • 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 synchronize your project.
  10. Sign up MachineLearning.ru. Send your login name to your coordinator (or to mlalgorithms [at] gmail [dot] com; to find the coordinator).
  11. Programming. Install Python Anaconda, PyCharm (alternative Visual Studio), Notebook online Google.Colab.
  12. Add. As alternative install and try Matlab (MIPT provides free version), (alternative Octave), R-project, Wofram Mathematica.
  13. Add. Read with pleasure Кутателадзе С. С. Советы эпизодическому переводчику and Сосинский А. Б. Как написать математическую статью по-английски (Ru).


Resources

To be updated after class, Feb 15th

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


Homework

  1. Fill the questionnaire week m1 – Imagine and plan a project
  2. Watch the video Моя первая научная статья 0 My first scientific paper, week 0
  3. Watch the video Риски и результаты в машинном обучении Risics and results in machine learning
  4. Rigorously follow all the steps of section Set the toolbox