Esport

Basic information about the course

  • The course is divided into several parts, each of which is separate from the others, at least in terms of points.
  • There has been a change in the exercises on my part, with a greater focus on esports games, specifically Counter-Strike 2.

Exercises

Attendance

  • Attendance at exercises is mandatory.
  • Please use email to excuse yourself from exercises.

Points

  • The exercises will focus on Counter-Strike 2 (CS2), including an introduction to the game, analysis, data prediction, and creating custom behavior for bots.
  • The Python programming language will be used for implementation, with libraries such as numpy, pandas, scikit-learn, keras, matplotlib, demoparser2, and jupyterlab. The implementation of custom behavior for CS2 bots will be done in the KeyValue3 (KV3) language.
  • Points can be earned for completing individual tasks.
  • A maximum of 15 points can be earned for this part:
    • Task 1 - Analysis of the CS2 demo file: 4 points.
    • Task 2 - Prediction using a neural network: 4 points.
    • Task 3 - Behavior trees for bots: 7 points.

Tasks

How to submit tasks

  • Always send your solution, including your login, to my email as:
    • a zipped file named after your login, for example, "abc0001.zip".
      • The archive must contain only the necessary files, such as Jupyter notebooks, images, kv3.
      • Do not attach demo files!
    • or a link to a repository with the project in a version control system such as GitHub.
  • Submission no later than the following exercise.

External resources