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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].
+
=== 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.
+
# 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).
+
# 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).
+
# 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).
+
# 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).
 
# Download [http://jmlda.org/papers/doc/jmlda-guides.zip the paper template, ZIP] and compile it.
 
# '''References'''. Read [http://en.wikipedia.org/wiki/Bibtex BibTeX].
 
#* [http://liinwww.ira.uka.de/csbib?strijov%20nonlinear An example of a bibliographic database].
 
#* [http://liinwww.ira.uka.de/cgi-bin/bibshow?e=Njtd0ECMQ03121/fyqboefe%7d81352582&r=bibtex&mode=intra An example of a bibliographic record].
 
#* [https://docs.google.com/document/d/10JgJMieX13R5vlrCfJPrMqU9Rsd4c4JzaGytU8rWFE4/edit?usp=sharing An example of draft review LinkReview].
 
#* [http://en.wikipedia.org/wiki/List_of_academic_databases_and_search_engines List of databases and search engines].
 
#* [https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research List of data-sets for Machine Learning projects].
 
# Install bibliographic collection software [http://jabref.sourceforge.net/ JabRef] (can be postponed).
 
# '''Communications'''. Sign up [https://github.com/ GitHub].
 
#* Run GitHub Copilot
 
#* Important: address and login like Name.Surname or Name-Surname (it depends on system conventions) is welcome.
 
#* Introductory sliders [http://www.machinelearning.ru/wiki/images/2/29/MMP_Praktikum317_2013s_VCS.pdf on Version Control System].
 
#* Introduction to [https://guides.github.com/ GitHub].
 
#* The first steps in [https://guides.github.com/activities/hello-world/ GitHub].
 
# Download a shell: [https://desktop.github.com/ Desktop.GitHub], or use a command line to synchronize your project.
 
# 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).
 
<!-- # Create your page [http://www.machinelearning.ru/wiki/index.php?title=%D0%A3%D1%87%D0%B0%D1%81%D1%82%D0%BD%D0%B8%D0%BA:Anastasiya example]. -->
 
<!--# To state a problem (write an essay) using notebook [https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Typesetting%20Equations.html see example] in MathJax.-->
 
<!-- # Поставить ссылку на личную страницу со своей фамилии в таблице на странице группы.-->
 
<!--# Install [https://hangouts.google.com/ Hangouts], [http://www.machinelearning.ru/wiki/index.php?title=%D0%A1%D0%BA%D0%B0%D0%B9%D0%BF_%28Skype%29 Skype - read instructions].-->
 
# '''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].
 
#* Development for ML: [https://pytorch.org/ PyTorch]
 
#* Style formatting: [https://www.python.org/dev/peps/pep-0008/ Codestyle pep8]
 
# '''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/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/images/1/18/MatlabStyle1p5.pdf Matlab Programming Style Guidelines].
 
# '''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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=== BibTeX ===
==Express your project view ==
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# Read [http://en.wikipedia.org/wiki/Bibtex BibTeX on Wiki].
* Write four lines: a goal and motivation (what and why?) for any project you believe is worth noting. Refer to [[Projects]].
+
<!-- # [http://en.wikipedia.org/wiki/List_of_academic_databases_and_search_engines List of databases and search engines].-->
* 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]].
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#* See an example  of a  [https://dblp.org/ bibliographic database]
* Go to Stepik:  
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#* and  an example of a [https://dblp.org/rec/journals/hisas/DorinKGS24.html?view=bibtex bibliographic record].
*# Sign up to the [https://stepik.org/catalog?auth=registration stepik.org].
+
# Create your draft LinkReview with an [https://docs.google.com/document/d/10JgJMieX13R5vlrCfJPrMqU9Rsd4c4JzaGytU8rWFE4/edit?usp=sharing example].
*# Run the course [https://stepik.org/course/90240/promo m1p].
+
# Install the bibliographic collection software [http://jabref.sourceforge.net/ JabRef].
*# Put your project description in Step 1.1.
+
 
*# Participate in the peer review.
+
===Collarobation===
-->
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#  Sign up [https://github.com/ GitHub].
 +
#* Set your username as: Name.Surname or Name-Surname.
 +
#* Take a step in [https://guides.github.com/activities/hello-world/ GitHub introduction] and look through [https://guides.github.com/ the GitHub docs].
 +
# Download [https://desktop.github.com/ Desktop.GitHub], or use [https://cli.github.com/manual/ the command line CLI] to synchronize your project.
 +
#* Read the slides [http://www.machinelearning.ru/wiki/images/2/29/MMP_Praktikum317_2013s_VCS.pdf (Ru) Version Control System].
 +
# Send your login name to your '''group coordinator''' to join [https://github.com/intsystems /github.com/intsystems].
 +
 
 +
===Programming===
 +
# Install [https://anaconda.org/anaconda/python Python Anaconda],
 +
# install [https://www.jetbrains.com/pycharm/ PyCharm] or [https://code.visualstudio.com/ Visual Studio],
 +
# and try [https://colab.research.google.com/notebooks/welcome.ipynb#recent=true Google Colab].
 +
# Look through [https://www.python.org/dev/peps/pep-0008/ Codestyle pep8].
 +
 
 +
To be informed of the variety of programming languages try one of the following online compiles:
 +
[https://fr.mathworks.com/products/matlab-online.html Matlab],
 +
[https://www.wolframcloud.com/ Mathematica],
 +
[https://julialang.org/ the Julia language],
 +
[https://www.r-project.org/ the R project].
 +
<!-- 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:
+
# 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==
+
==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].
+
# Rigorously follow the guide of Section [[Week_1#Select_your_project|Select your project]]
Obsoleted
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# Take the steps of Section [[Week_1#Set_the_toolbox|Set the toolbox]]
* [http://www.machinelearning.ru/wiki/images/c/c9/M1p_lect0.pdf Slides for week 0].
+
# Write your coordinator and get access to the [https://github.com/intsystems GitHub repositories]
* [https://www.youtube.com/watch?v=vRUYqnas5fo Video for week 0].
+
# Open your notebook in the LinkReview format to gather your notes, thoughts, and references about your project.
* [http://www.machinelearning.ru/wiki/images/d/dc/M1p_lect1.pdf Slides for week 1].
+
#* 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].
* [https://www.youtube.com/watch?v=EhgNePTkMkE Video for week 1].
 
* [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==
# [https://arxiv.org/pdf/1709.02840 A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018]
+
# [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
# [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]
+
# [http://www.inference.org.uk/itprnn/book.pdf Information Theory, Pattern Recognition and Neural Networks] by D. MackKay, 2009
# [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://arxiv.org/pdf/1709.02840 A Brief Introduction to Machine Learning for Engineers] by Osvaldo Simeone, 2018
# [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://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://unglueit-files.s3.amazonaws.com/ebf/617027d14a3046998f54b31503ab7bca.pdf Python notes for professionals by GoalKicker.com Free Programming Books.]
+
# [https://mml-book.github.io/book/mml-book.pdf Mathematics for Machine Learning] by M. Peter et al.  
# [http://www.1variant.ru/content/uchebniki/matematika/650.pdf Лагутин М.Б. Наглядная математическая статистика, М.: Бином, 2009.] См. также [http://files.lbz.ru/pdf/cC2125-4-ch.pdf вырезку] (Ru)
+
# [https://klassfeldtheorie.files.wordpress.com/2018/10/mathematische-methoden-310117.pdf Mathematics for Physicists] by Alexander Altland & Jan von Delf, 2017
# [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]
+
# [https://unglueit-files.s3.amazonaws.com/ebf/617027d14a3046998f54b31503ab7bca.pdf Python notes for professionals] by GoalKicker.com Free Programming Books
# [http://www.inference.org.uk/itprnn/book.pdf MackKay D. Information Theory, Pattern Recognition and Neural Networks, Inference.org.uk, 2009]
+
# Computer Mathematics by D.J. Cooke and H.E. Bez, 1984 (Ru [https://www.phantastike.com/math/comriuternaya_matematika_cooke/pdf/ Кук, Бейз])
 
 
==Homework==
 
# [https://forms.gle/FEJ28KEjxdj6Zgha7 Fill the questionnaire week m1] – Imagine and plan a project
 
# Watch [https://www.youtube.com/watch?v=VNgm-oXENnc&t=627s video]
 
# 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'''
 
# ''Check'' if you can briefly explain what is
 
## [https://en.wikipedia.org/wiki/Test_statistic statistical hypothesis]
 
## [https://en.wikipedia.org/wiki/Maximum_likelihood_estimation statistical inference],
 
## [https://en.wikipedia.org/wiki/Conditional_probability_distribution conditional] and [https://en.wikipedia.org/wiki/Joint_probability_distribution joint] distributions,
 
## [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.-->
 
# 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 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)