Week 4

Цель этой недели — в самый краткий срок получить, самое простое решение (модель и ее параметры), поставленной задачи.

X: Experiment planning

Plan your computational experiment.

1. Discuss the experiment goal with your adviser and team.
   • Put this goal in the section Computational experiment
2. Describe your basic data set, a synthetic, or a simple real one:
   • put in the text the title, source and set up of measurements (it is the technical description, the theoretical one is in the problem statement section),
   • write down the number of objects, features, describe general statistics,
   • for a synthetic data set describe the generation model, its parameters (for example, uniform random independent sampling some given interval).
3. Describe the configuration of algorithm run.
4. Plan the whole experimental part.
5. List expected tables and figures:
   • make short and long list, for each
   • describe axes,
   • make a draft with a pencil.

R: Preliminary report

1. Make sure that the obtained results are in not logical (sic!) contradiction with the goals of the computational experiment.
2. Illustrate the obtained results with the preliminary plot see the format.
3. Write a mini-report on the results with
   1. a short description of the figure: what the reader could see, what are the consequences,
   2. the results in numbers and comments on it,
   3. put the report to the section computational experiment.

B: Run basic code

Select the basic algorithm and run it using a simple data set.

1. Run your basic algorithm:
   • select a simplest algorithm (with your adviser) to (partially) solve the problem you set.
2. Collect a synthetic data set or download a simple real-word data set of small size.
3. Upload your data to the repository (in case the data size exceed 5MB or the data set consists of numerous files, please discuss with your adviser and team).
4. Run the basic algorithm on the synthetic data set, estimate the error.
5. Describe the basic algorithm, analyst its features, list competitive models.
   1. Описание - указание на название черного ящика. Желательно указывать на источник, где содержимое черного ящика описывается подробно. Указывать структурные параметры черного ящика.
   2. Описание модели как отображения из пространства описания объектов в пространство целевых переменных. При этом можно указать на алгоритм оптимизации параметров модели в виде черного ящика.
   3. Описание модели и алгоритма оптимизации его параметров в виде псевдокода.

Resources

• Slides for week 4.
• Video for week 4.
• Бахтеев О.Ю. Системы и средства глубокого обучения, статья
• Мотренко А.П. Повышение качества классификации, статья
• Исаченко Р.В. Снижение размерности в задаче декодирования, статья
• The goals of computational experiments А. Грабовой, В. Алексеев, А. Рогозина, И. Игашов, Н. Уваров
• Example of the measurement description, Bishop C.P. Pattern recognition and machine learning, 2006. Pp. 677-683.]