Creation of Intelligent Systems
This is the extension of “Automation of Scientific Research” course. The work is organized in teams (2-3 people). Students will prepare weekly presentation of the team’s work: 5 minutes presentation + 5 minutes discussion with other students.
Criteria for the project:
- The entire project must be on GitHub under an OpenSource license (MIT)
- The project must contain the code and instructions for launching it.
- The basic code in jupyter notebook to demonstrate the concept of work and plotting from the article should be run with colab.
- The source code of the computational experiment should be run on a Unix system by executing two commands (possibly in a special docker image):
- python3 train.py - for training models.
- python3 test.py - for testing, obtaining the results of the experiment.
- The documentation for the code is sphinx.
- The project should contain the manuscript of the article using a stylistic from arXiv - for unification.
- If the project implies non-synthetic data, then there should be an instruction for obtaining this data, as well as a script for obtaining them. If the data is specific, then they need to be posted on one of the file storages.
- Introductory lecture.
- Projects discussion.
- Project structure.
- The first checkpoint.
- Paper structure.
- How to build a jupiter notebook correctly.
- The second checkpoint.
- The correct code is not in jupiter notebook!
- Data storage for an experiment.
- The third checkpoint.
- Code documentation using sphinx.
- Docker for the experiment code.
- The fourth checkpoint.
Writing scientific paper.
There are 4 checkpoints in the course, 3 points are given for each
- Research of the subject area - analysis of the problem.
- Theoretical results.
- Computational experiment.
- Github project.
Machine learning, deep learning, Python.