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|description=Course My first scientific paper: The goal of this week is to set up your tools, and to select your project}}
 
|description=Course My first scientific paper: The goal of this week is to set up your tools, and to select your project}}
  
''The goal of this week'' is to set up your tools, and to select your project.  
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''The goal of this week'' is to set up your tools and select your project.
  
 
==Set the toolbox==  
 
==Set the toolbox==  
# '''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].
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=== LaTeX ===
# 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.
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# Install the LaTeX compiler: [http://miktex.org MikTeX] for Windows, [http://www.tug.org/texlive/ TeX Live] for Linux, and Mac OS. Sign up [https://v2.overleaf.com/ OverLeaf].
#* Read ''Львовский С.М.'' [http://www.mccme.ru/free-books/llang/newllang.pdf Набор и верстка в системе LaTeX] (Ru).
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# Install the editor [http://www.texniccenter.org/ TeXnic Center] or [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).
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# Read [https://tobi.oetiker.ch/lshort/lshort.pdf Introduction to LATEX] by Oetker et al., 2023 or (Ru [http://www.mccme.ru/free-books/llang/newllang.pdf Львовский С.М.].
#* 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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# Download the [https://github.com/vadim-vic/the-Art-homework?tab=readme-ov-file paper template] and compile it. You need two files: [https://github.com/vadim-vic/the-Art-homework/blob/main/Name-Step-1.tex .tex] and [https://github.com/vadim-vic/the-Art-homework/blob/main/Name-theArt.bib .bib]
#* Note ''Мильчин А.Э. Чельцова Л.К. ''[https://orfogrammka.ru/%D1%81%D0%BF%D1%80%D0%B0%D0%B2%D0%BE%D1%87%D0%BD%D0%B8%D0%BA/%D1%81%D0%BF%D1%80%D0%B0%D0%B2%D0%BE%D1%87%D0%BD%D0%B8%D0%BA_%D0%B8%D0%B7%D0%B4%D0%B0%D1%82%D0%B5%D0%BB%D1%8F_%D0%B8_%D0%B0%D0%B2%D1%82%D0%BE%D1%80%D0%B0_%D0%BC%D0%B8%D0%BB%D1%8C%D1%87%D0%B8%D0%BD_%D1%87%D0%B5%D0%BB%D1%8C%D1%86%D0%BE%D0%B2%D0%B0/  Справочник издателя и автора] (Ru).
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# Download the [https://github.com/vadim-vic/the-Art-homework?tab=readme-ov-file the paper template] and compile it. You need two files: [https://github.com/vadim-vic/the-Art-homework/blob/main/Name-Step-1.tex .tex] and [https://github.com/vadim-vic/the-Art-homework/blob/main/Name-theArt.bib .bib]
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=== BibTeX ===
# '''References'''. Read [http://en.wikipedia.org/wiki/Bibtex BibTeX].
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# Read [http://en.wikipedia.org/wiki/Bibtex BibTeX on Wiki].
#* [https://dblp.org/ An example of a bibliographic database].
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<!-- # [http://en.wikipedia.org/wiki/List_of_academic_databases_and_search_engines List of databases and search engines].-->
#* [https://dblp.org/rec/journals/hisas/DorinKGS24.html?view=bibtex An example of a bibliographic record].
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#* See an example  of a  [https://dblp.org/ bibliographic database]
#* [https://docs.google.com/document/d/10JgJMieX13R5vlrCfJPrMqU9Rsd4c4JzaGytU8rWFE4/edit?usp=sharing An example of draft review LinkReview].
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#* and  an example of a [https://dblp.org/rec/journals/hisas/DorinKGS24.html?view=bibtex bibliographic record].
#* [http://en.wikipedia.org/wiki/List_of_academic_databases_and_search_engines List of databases and search engines].
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# Create your draft LinkReview with an [https://docs.google.com/document/d/10JgJMieX13R5vlrCfJPrMqU9Rsd4c4JzaGytU8rWFE4/edit?usp=sharing example].
#* [https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research List of data-sets for Machine Learning projects].
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# Install the bibliographic collection software [http://jabref.sourceforge.net/ JabRef].
# Install bibliographic collection software [http://jabref.sourceforge.net/ JabRef].
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# '''Communications'''. Sign up [https://github.com/ GitHub].
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===Collarobation===
#* Run GitHub Copilot
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# Sign up [https://github.com/ GitHub].
#* Important: address and login like Name.Surname or Name-Surname (it depends on system conventions) is welcome.
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#* Set your username as: Name.Surname or Name-Surname.
#* Introductory sliders [http://www.machinelearning.ru/wiki/images/2/29/MMP_Praktikum317_2013s_VCS.pdf on Version Control System] (ru).
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#* Take a step in [https://guides.github.com/activities/hello-world/ GitHub introduction] and look through [https://guides.github.com/ the GitHub docs].  
#* Introduction to [https://guides.github.com/ GitHub].
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# Download [https://desktop.github.com/ Desktop.GitHub], or use [https://cli.github.com/manual/ the command line CLI] to synchronize your project.
#* The first steps in [https://guides.github.com/activities/hello-world/ GitHub].
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#* Read the slides [http://www.machinelearning.ru/wiki/images/2/29/MMP_Praktikum317_2013s_VCS.pdf (Ru) Version Control System].
# Download a shell: [https://desktop.github.com/ Desktop.GitHub], or use [https://cli.github.com/manual/ the command line CLI] to synchronize your project.
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# Send your login name to your '''group coordinator''' to join [https://github.com/intsystems /github.com/intsystems].
# Sign up [http://www.machinelearning.ru/ MachineLearning.ru]. Send your login name to your coordinator (or to mlalgorithms [at] gmail [dot] com; to find the coordinator).
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# '''Programming'''. Install Python [https://anaconda.org/anaconda/python Anaconda], [https://www.jetbrains.com/pycharm/ PyCharm] (alternative [https://code.visualstudio.com/ Visual Studio]), Notebook online [https://colab.research.google.com/notebooks/welcome.ipynb#recent=true Google.Colab].
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===Programming===
#* Development for ML: [https://pytorch.org/ PyTorch]
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# Install [https://anaconda.org/anaconda/python Python Anaconda],  
#* Style formatting: [https://www.python.org/dev/peps/pep-0008/ Codestyle pep8]
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# install [https://www.jetbrains.com/pycharm/ PyCharm] or [https://code.visualstudio.com/ Visual Studio],  
# '''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].
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# and try [https://colab.research.google.com/notebooks/welcome.ipynb#recent=true Google Colab].
#* Read [http://www.machinelearning.ru/wiki/images/archive/f/fc/20150209132356%21Voron-ML-Intro-slides.pdf Introduction to Matlab]].
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# Look through [https://www.python.org/dev/peps/pep-0008/ Codestyle pep8].
#* 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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#* Read [http://www.machinelearning.ru/wiki/images/1/18/MatlabStyle1p5.pdf Matlab Programming Style Guidelines].
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To be informed of the variety of programming languages try one of the following online compiles:
# '''Add.''' See [https://julialang.org/ The Julia language] and [https://www.r-project.org/The R project]
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[https://fr.mathworks.com/products/matlab-online.html Matlab],  
# '''Add.''' Read with pleasure [http://www.math.nsc.ru/LBRT/g2/english/ssk/r-e.pdf Кутателадзе С. С. Советы эпизодическому переводчику] and [http://www.ega-math.narod.ru/Quant/ABS.htm Сосинский А. Б. Как написать математическую статью по-английски] (Ru).
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[https://www.wolframcloud.com/ Mathematica],
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[https://julialang.org/ the Julia language],
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[https://www.r-project.org/ the R project].
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<!-- Read [http://www.machinelearning.ru/wiki/images/1/18/MatlabStyle1p5.pdf Matlab Programming Style Guidelines].-->
  
 
== Select your project ==  
 
== Select your project ==  
To select your project:
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# Look through the list of projects [https://github.com/intsystems/m1p/blob/main-2025/problem_list.md (Spring 2025)].
# [https://github.com/intsystems/m1p/blob/main-2025/problem_list.md Look through the list of projects (Spring 2025)].
 
 
# Find public information about the experts and consultants.
 
# Find public information about the experts and consultants.
 
# Select your projects during the group discussion.
 
# Select your projects during the group discussion.
# Wait for confirmation from '''the responsible  coordinator''' of your student group
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# Wait for confirmation from your '''group coordinator''' of your student group.
# Put confirmed topics [https://github.com/intsystems/m1p/blob/main-2025/ Group table (Spring 2025)].
 
 
# Politely write your consultant and discuss your project.
 
# Politely write your consultant and discuss your project.
  
==Resources==
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==Homework==
* [https://www.youtube.com/watch?v=VNgm-oXENnc&t=627s Video week 1]
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# Fill in the questionnaire for [https://forms.gle/FEJ28KEjxdj6Zgha7  Week 1: Imagine and plan a project]
* [http://www.machinelearning.ru/wiki/images/d/dc/m1p_2024_lect1.pdf Slides week 1].
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# Rigorously follow the guide of Section [[Week_1#Select_your_project|Select your project]]
* [http://www.machinelearning.ru/wiki/images/c/c9/M1p_lect0.pdf Slides for week 0].
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# Take the steps of Section [[Week_1#Set_the_toolbox|Set the toolbox]]
* [https://www.youtube.com/watch?v=vRUYqnas5fo Video for week 0].
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# Write your coordinator and get access to the [https://github.com/intsystems GitHub repositories]
* [http://www.machinelearning.ru/wiki/images/d/dc/M1p_lect1.pdf Slides for week 1].
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# Open your notebook in the LinkReview format to gather your notes, thoughts, and references about your project.
* [https://www.youtube.com/watch?v=EhgNePTkMkE Video for week 1].
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#* See more examples of LinkReview: [https://docs.google.com/document/d/1K7bIzU33MSfeUvg3WITRZX0pe3sibbtH62aw42wxsEI/edit?tab=t.0 one], [https://docs.google.com/document/d/1PflOzE5M3fD4BuChB0-OKnutdMrWBpuQAvkSwyBNv8Y/edit?tab=t.0 two], [https://docs.google.com/document/d/1b3ZF635fTWMtB35_slSHpfUMXgyFZAv7CpMUPhHQ5Hk/edit?tab=t.0#heading=h.beiz6lk1991d three].
* [http://svn.code.sf.net/p/mvr/code/lectures/MLEducation/Strijov2014MLCourseShort.pdf?format=raw Short course description].
 
  
 
==References to catch up==
 
==References to catch up==
# [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] or see [https://www.bishopbook.com/ the version 2024] on Deep Learning
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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 Pattern recognition and machine learning] by C.P. Bishop, or the version  on [https://www.bishopbook.com/ Deep Learning], 2024
# [http://www.inference.org.uk/itprnn/book.pdf MackKay D. Information Theory, Pattern Recognition and Neural Networks, Inference.org.uk, 2009]
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# [http://www.inference.org.uk/itprnn/book.pdf Information Theory, Pattern Recognition and Neural Networks] by D. MackKay, 2009
# [https://arxiv.org/pdf/1709.02840 A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018]
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# [https://arxiv.org/pdf/1709.02840 A Brief Introduction to Machine Learning for Engineers] by Osvaldo Simeone, 2018
# [https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David, 2014]
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# [https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf 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]
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# [https://mml-book.github.io/book/mml-book.pdf Mathematics for Machine Learning] by M. Peter et al.  
# [https://klassfeldtheorie.files.wordpress.com/2018/10/mathematische-methoden-310117.pdf Mathematics for Physicists: Introductory Concepts and Methods by Alexander Altland & Jan von Delf]
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# [https://klassfeldtheorie.files.wordpress.com/2018/10/mathematische-methoden-310117.pdf Mathematics for Physicists] by Alexander Altland & Jan von Delf, 2017
# [https://unglueit-files.s3.amazonaws.com/ebf/617027d14a3046998f54b31503ab7bca.pdf Python notes for professionals by GoalKicker.com Free Programming Books.]
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# [https://unglueit-files.s3.amazonaws.com/ebf/617027d14a3046998f54b31503ab7bca.pdf Python notes for professionals] by GoalKicker.com Free Programming Books
 
# Computer Mathematics by D.J. Cooke and H.E. Bez, 1984 (Ru [https://www.phantastike.com/math/comriuternaya_matematika_cooke/pdf/ Кук, Бейз])
 
# Computer Mathematics by D.J. Cooke and H.E. Bez, 1984 (Ru [https://www.phantastike.com/math/comriuternaya_matematika_cooke/pdf/ Кук, Бейз])
 
==Homework==
 
# Fill in the questionnaire of [https://forms.gle/FEJ28KEjxdj6Zgha7  Week 1: Imagine and plan a project]
 
# Run all steps of Section [[Week_1#Select_your_project|Select your project]]
 
# Rigorously follow all the steps of Section [[Week_1#Set_the_toolbox|Set the toolbox]]
 
# Write your coordinator and get access to the GitHub
 
# Join your inherited project repository (recommended) or create a new one (here will be the template)
 

Latest revision as of 23:07, 21 February 2025

The goal of this week is to set up your tools and select your project.

Set the toolbox

LaTeX

  1. Install the LaTeX compiler: MikTeX for Windows, TeX Live for Linux, and Mac OS. Sign up OverLeaf.
  2. Install the editor TeXnic Center or WinEdt for Windows, TeXworks for Linux, and TeXmaker for Mac OS.
  3. Read Introduction to LATEX by Oetker et al., 2023 or (Ru Львовский С.М..
  4. Download the paper template and compile it. You need two files: .tex and .bib

BibTeX

  1. Read BibTeX on Wiki.
  2. Create your draft LinkReview with an example.
  3. Install the bibliographic collection software JabRef.

Collarobation

  1. Sign up GitHub.
  2. Download Desktop.GitHub, or use the command line CLI to synchronize your project.
  3. Send your login name to your group coordinator to join /github.com/intsystems.

Programming

  1. Install Python Anaconda,
  2. install PyCharm or Visual Studio,
  3. and try Google Colab.
  4. Look through Codestyle pep8.

To be informed of the variety of programming languages try one of the following online compiles: Matlab, Mathematica, the Julia language, the R project.

Select your project

  1. Look through the list of projects (Spring 2025).
  2. Find public information about the experts and consultants.
  3. Select your projects during the group discussion.
  4. Wait for confirmation from your group coordinator of your student group.
  5. Politely write your consultant and discuss your project.

Homework

  1. Fill in the questionnaire for Week 1: Imagine and plan a project
  2. Rigorously follow the guide of Section Select your project
  3. Take the steps of Section Set the toolbox
  4. Write your coordinator and get access to the GitHub repositories
  5. Open your notebook in the LinkReview format to gather your notes, thoughts, and references about your project.

References to catch up

  1. Pattern recognition and machine learning by C.P. Bishop, or the version on Deep Learning, 2024
  2. Information Theory, Pattern Recognition and Neural Networks by D. MackKay, 2009
  3. A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2018
  4. Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David, 2014
  5. Mathematics for Machine Learning by M. Peter et al.
  6. Mathematics for Physicists by Alexander Altland & Jan von Delf, 2017
  7. Python notes for professionals by GoalKicker.com Free Programming Books
  8. Computer Mathematics by D.J. Cooke and H.E. Bez, 1984 (Ru Кук, Бейз)