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 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.
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
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.
- Download the paper template, ZIP and compile it.
- Read BibTeX.
- Install bibliographic collection software JabRef (can be postponed).
- Communications. Sign up 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.
- Announcements: Telegram m1p_news
- Ask to email mlalgorithms [at] gmail [dot] com
- 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.