Week 1

The goal of this week is to set up your tools, and to 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.
   • See an example of a bibliographic database
   • and an example of a bibliographic record.
2. Create your draft LinkReview with an example.
3. Install bibliographic collection software JabRef.

Collarobarion

1. Sign up GitHub.
   • Set your address and login as: Name.Surname or Name-Surname.
   • Thake a step in GitHub introduction and look through the GitHub docs.
2. Download Desktop.GitHub, or use the command line CLI to synchronize your project.
   • Read the slides (Ru) Version Control System.
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. 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 you group coordinator of your student group.
5. Politely write your consultant and discuss your project.

Homework

1. Fill in the questionnaire of 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)