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− | '''The goal of this week'''is comprehend the goal at its whole and write about it. | + | '''The goal of this week''' is comprehend the goal at its whole and write about it. |
'''Resources''' | '''Resources''' |
Revision as of 18:37, 25 February 2020
The todo lists here corresponds to the Course schedule. Each list must be completed before the day of review. It is Wednesday 06:00 am for the 2020 Spring semester.
Contents
Todo A: Abstract
- Write a draft of your abstract.
- The abstract shall not exceed 600 characters. It may contain:
- wide-range field of the investigated problem,
- narrow problem to focus on,
- features and conditions of the problem,
- [the novelty],
- application to illustrate with.
- For joint projects it is important that each team-member writes its own text.
Resources
- How to Read a Paper, 2016, S. Keshav
- Examples of rewiev-and-planning drafts LinkReview раз, два.
Todo B: Beginner's-talk
Short 45-second introductory talk. Plan of the talk:
- The project goal. What is the motivation, the goal to reach?
- The main idea. What is the message?
- The expected result. What is your delivery, your impact, novelty?
There is no time to show a slide or draw a plot on the blackboard. It is recommended to rehearse the report.
Todo I: Introduction
The introductory part includes research goals and motivations. It reasons the research with fundamental and state-of-the-arts references. It delivers the main message of the work to the reader. This message shows novelty of this work in comparison to recent results.
- Create a file ProjectN.bib for the group project, or Surname2018Title.bib for your personal prpject.
- Move from the file LinkReview useful bibliographic records in the BibTeX format.
- Check the correctness of the BibTeX database (styles of authors names, volumes of journals, page numbers).
- Use bibliographic databases to facilitate your work.
- Use the default style \bibliographystyle{plain} before the bibliography section \bibliography{ProjectN}.
- Important! Wikipedia is not the source of information, but it contains many useful sources.
- Important! ArXiv is not a peer-review source of information. Look for the copies of papers that are published in peer-review scientific joournals. If after one or two years after its ArXiv version, the pare did not appear in a peer-review journal, be careful to use it: this paper might be non-verified since it was rejected by the other journals.
- Write Introduction. The expected size is one page. The expected plan is:
- the research goal (and its motivations),
- the object of research (introduce main termini),
- the problem (what is the challenge),
- methodology: literature review and state-of-the-art
- the project tasks,
- the proposed solution, its novelty and advantages,
- the profs and cons of recent works,
- goal of the experiment, set up, data sets, workflow.
The goal of this week is comprehend the goal at its whole and write about it.
Resources
- Bibliographic databases
- The Collection of Computer Science Bibliographies
- List of academic databases and search engines in Wikipedia
- Refer to BibTeX in Wikipedia
- An introduction updated after a peeer-rewiev.
Todo L: Literature
- * The link
Todo 1: Select your project
To select your project:
- Look through the list of projects.
- Find information about the experts and consultants.
- Select your projects in the questionnaire before Wednesday 22:00pm.
- Wait for confirmation.
- Put confirmed topics to the Group table on Machine learning
Todo 0: Prepare necessary tools
- Editing. Install LaTeX: MikTeX for Windown, TeX Live for Linux, and for Mac OS. Sign up V2 OverLeaf ShareLaTeX.
- 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.
- Download the paper template, ZIP and compile it.
- Read BibTeX.
- Install bibliographic collection software JabRef (can be postponed).
- 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.
- Download a shell: Desktop.GitHub, or use a command line to synchronise your project.
- Sign up MachineLearning.ru. Send a logon to your coordinator of to mlalgorithms [at] gmail [dot] com.
- To state a problem (write essay) using notebook see example in MathJax.
- Create your page example.
- Install Hangouts, Skype - read instructions.
- Programming. Install Python Anaconda, PyCharm (alternative Visual Studio), Notebook online Google.Colab.
- Development for ML: PyTorch
- Style formatting: Codestyle pep8
- Add. As alternative install and try Matlab (MIPT provides free version), (alternative Octave), R-project, Wofram Mathematica.
- Add. Read with pleasure Кутателадзе С. С. Советы эпизодическому переводчику and Сосинский А. Б. Как написать математическую статью по-английски.
Resources
- Announcements: Telegram m1p_news
- Ask to email mlalgorithms [at] gmail [dot] com
- Slides.
- Short course description.
References to catch up
- A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018
- Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David, 2014
- Mathematics for Machine learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
- Mathematics for Physicists: Introductory Concepts and Methods by Alexander Altland & Jan von Delf
- Python notes for professionals by GoalKicker.com Free Programming Books.
- Лагутин М.Б. Наглядная математическая статистика, М.: Бином, 2009. См. также вырезку.
- Bishop C.P. Pattern recognition and machine learning, Berlin: Springer, 2008.
- MackKay D. Information Theory, Pattern Recognition and Neural Networks, Inference.org.uk, 2009.
Todo -1: Subscribe to the course
Todo before 06:00 Wednesday, February 12 th:
- pick up a problem from the page Try-on programming problems (get the oldest problems, they are simpler),
- plot one figure to illustrate the problem (plot data or analysis),
- write explanatory comments to the figure (what the reader sees on the figure, what conclusions follow up),
- an example of the figure formatting is here
- upload your notebook to your github repository,
- send the link to this notebook to mlalgorithms [at] gmail [dot] com, with the subject "Application m1p"
- Example of a nice simple problem: bread regression.
- Examples of plots: one many solutions from this project.
- Examples of old problems Problem 7, Problem 1, Problem15.