Todo list
From Research management course
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.
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.
- Inportant: 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 Anakonda, 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
- Announcenents: Telegram AutomationML
- Ask to email mlalgorithms [at] gmail [dot] com
- Slides.
- Short course description.
- Progress questionary Todo 1.
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.