Difference between revisions of "Week 1"

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|title=Course My first scientific paper: Week 1
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|keywords=My first scientific paper
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|description=Course My first scientific paper: The goal of the week is to express your project view, and set the toolbox.
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}}
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==Set the toolbox==  
 
==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://v2.overleaf.com/ V2 OverLeaf  ShareLaTeX].
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# '''Editors'''. Install LaTeX: [http://miktex.org MikTeX] for Windows, [http://www.tug.org/texlive/ TeX Live] for Linux, and 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.
 
#* 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] (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] (Ru).  
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# '''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 Windows, [http://www.tug.org/texlive/ TeX Live] for Linux, and 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.
 
#* 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).
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#* Development for ML: [https://pytorch.org/ PyTorch]
 
#* Development for ML: [https://pytorch.org/ PyTorch]
 
#* Style formatting: [https://www.python.org/dev/peps/pep-0008/ Codestyle pep8]
 
#* Style formatting: [https://www.python.org/dev/peps/pep-0008/ Codestyle pep8]
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# '''Add.''' As alternative install and try [http://www.machinelearning.ru/wiki/index.php?title=Matlab Matlab (MIPT provides free version)], (alternative [http://www.gnu.org/software/octave/ Octave]), [https://www.r-project.org/ R-project], [https://www.wolframcloud.com/ Wofram Mathematica].
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# '''Add.''' As alternative install and try [http://www.machinelearning.ru/wiki/index.php?title=Matlab Matlab (check of your university provides a free version)], (alternative [http://www.gnu.org/software/octave/ Octave]), [https://www.r-project.org/ R-project], [https://www.wolframcloud.com/ Wolfram Mathematica].
 
#* Read [http://www.machinelearning.ru/wiki/images/archive/f/fc/20150209132356%21Voron-ML-Intro-slides.pdf Introduction to Matlab]].
 
#* Read [http://www.machinelearning.ru/wiki/images/archive/f/fc/20150209132356%21Voron-ML-Intro-slides.pdf Introduction to Matlab]].
 
#* Read [http://www.machinelearning.ru/wiki/index.php?title=%D0%94%D0%BE%D0%BA%D1%83%D0%BC%D0%B5%D0%BD%D1%82%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5_%D1%84%D1%83%D0%BD%D0%BA%D1%86%D0%B8%D0%B9_Matlab Matlab code style, reporting and documenting]].
 
#* Read [http://www.machinelearning.ru/wiki/index.php?title=%D0%94%D0%BE%D0%BA%D1%83%D0%BC%D0%B5%D0%BD%D1%82%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5_%D1%84%D1%83%D0%BD%D0%BA%D1%86%D0%B8%D0%B9_Matlab Matlab code style, reporting and documenting]].
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==Express your project view ==
 
==Express your project view ==
 
* Write four lines: a goal and motivation (what and why?) for any project you believe is worth noting. Refer to [[Projects]].
 
* Write four lines: a goal and motivation (what and why?) for any project you believe is worth noting. Refer to [[Projects]].
−
* If you have subscribed to this course with your own project: please connect your advisor or consultant and write your project description as shown in the [[Projects|project description template]].
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* If you have subscribed to this course with your project: please connect your advisor or consultant and write your project description as shown in the [[Projects|project description template]].
 
* Go to Stepik:  
 
* Go to Stepik:  
 
*# Sign up to the [https://stepik.org/catalog?auth=registration stepik.org].
 
*# Sign up to the [https://stepik.org/catalog?auth=registration stepik.org].
 
*# Run the course [https://stepik.org/course/90240/promo m1p].
 
*# Run the course [https://stepik.org/course/90240/promo m1p].
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*# Put your project description to Step 1.1.
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*# Put your project description in Step 1.1.
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*# Participate to the peer review.
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*# Participate in the peer review.
 
-->
 
-->
  
 
==Resources==
 
==Resources==
 +
* [https://www.youtube.com/watch?v=VNgm-oXENnc&t=627s Video week 1]
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* [http://www.machinelearning.ru/wiki/images/d/dc/m1p_2024_lect1.pdf Slides week 1].
 +
Obsoleted
 
* [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].
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# [https://arxiv.org/pdf/1709.02840 A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018]
 
# [https://arxiv.org/pdf/1709.02840 A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018]
 
# [https://www.semanticscholar.org/paper/Understanding-Machine-Learning%3A-From-Theory-to-Shalev-Shwartz-Ben-David/ce615ae61d67db8537e981a0a08da7f0f2ff1cee Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David, 2014]
 
# [https://www.semanticscholar.org/paper/Understanding-Machine-Learning%3A-From-Theory-to-Shalev-Shwartz-Ben-David/ce615ae61d67db8537e981a0a08da7f0f2ff1cee Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David, 2014]
−
# [https://mml-book.github.io/book/mml-book.pdf Mathematics for Machine learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong]
+
# [https://mml-book.github.io/book/mml-book.pdf Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong]
 
# [https://klassfeldtheorie.files.wordpress.com/2018/10/mathematische-methoden-310117.pdf Mathematics for Physicists: Introductory Concepts and Methods by Alexander Altland & Jan von Delf]
 
# [https://klassfeldtheorie.files.wordpress.com/2018/10/mathematische-methoden-310117.pdf Mathematics for Physicists: Introductory Concepts and Methods by Alexander Altland & Jan von Delf]
 
# [https://unglueit-files.s3.amazonaws.com/ebf/617027d14a3046998f54b31503ab7bca.pdf Python notes for professionals by GoalKicker.com Free Programming Books.]  
 
# [https://unglueit-files.s3.amazonaws.com/ebf/617027d14a3046998f54b31503ab7bca.pdf Python notes for professionals by GoalKicker.com Free Programming Books.]  
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# [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop C.P. Pattern recognition and machine learning, Berlin: Springer, 2008]
 
# [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop C.P. Pattern recognition and machine learning, Berlin: Springer, 2008]
 
# [http://www.inference.org.uk/itprnn/book.pdf MackKay D. Information Theory, Pattern Recognition and Neural Networks, Inference.org.uk, 2009]
 
# [http://www.inference.org.uk/itprnn/book.pdf MackKay D. Information Theory, Pattern Recognition and Neural Networks, Inference.org.uk, 2009]
−
 
−
 
  
 
==Homework==
 
==Homework==
 
# [https://forms.gle/FEJ28KEjxdj6Zgha7 Fill the questionnaire week m1] – Imagine and plan a project
 
# [https://forms.gle/FEJ28KEjxdj6Zgha7 Fill the questionnaire week m1] – Imagine and plan a project
−
# Briefly look through the materials of [[Week 0]] and [[Week 1]].
+
# Watch [https://www.youtube.com/watch?v=VNgm-oXENnc&t=627s video]  
−
# Watch the video [https://www.youtube.com/watch?v=vRUYqnas5fo Моя первая научная статья 0] My first scientific paper, week 0
 
−
 
 
 
# Watch the video [https://youtu.be/eXiXxmz3lnA Риски и результаты в машинном обучении] Risics and results in machine learning
 
# Watch the video [https://youtu.be/eXiXxmz3lnA Риски и результаты в машинном обучении] Risics and results in machine learning
 
# ''Rigorously'' follow all the steps of section '''Set the toolbox'''
 
# ''Rigorously'' follow all the steps of section '''Set the toolbox'''
−
<!-- # ''Quis'' Can you briefly explain what is (check means yes)
1) statistical hypothesis
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# ''Check'' if you can briefly explain what is  
−
2) statistical inference,
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## [https://en.wikipedia.org/wiki/Test_statistic statistical hypothesis]
−
3) conditional and joint distributions,
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## [https://en.wikipedia.org/wiki/Maximum_likelihood_estimation statistical inference],
−
4) likelihood function,
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## [https://en.wikipedia.org/wiki/Conditional_probability_distribution conditional] and [https://en.wikipedia.org/wiki/Joint_probability_distribution joint] distributions,
−
5) algebraic structure -->
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## [https://en.wikipedia.org/wiki/Likelihood_function likelihood function],
 +
## [https://en.wikipedia.org/wiki/Algebraic_structure algebraic structure]
 +
 
 +
 
 +
<!--# Look through the materials of [[Week 0]] and [[Week 1]] and the slides.-->
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# Watch the video [https://www.youtube.com/watch?v=vRUYqnas5fo Моя первая научная статья 0] My first scientific paper, week 0

Latest revision as of 15:05, 13 August 2024

Set the toolbox

  1. Editors. Install LaTeX: MikTeX for Windows, TeX Live for Linux, and 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 Windows, TeX Live for Linux, and 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 (check of your university provides a free version), (alternative Octave), R-project, Wolfram Mathematica.
  13. Add. Read with pleasure Кутателадзе С. С. Советы эпизодическому переводчику and Сосинский А. Б. Как написать математическую статью по-английски (Ru).


Resources

Obsoleted

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 video
  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
  5. Check if you can briefly explain what is
    1. statistical hypothesis
    2. statistical inference,
    3. conditional and joint distributions,
    4. likelihood function,
    5. algebraic structure


  1. Watch the video Моя первая научная статья 0 My first scientific paper, week 0
Retrieved from ‘https://m1p.org/index.php?title=Week_1&oldid=1584’