2026/2027
Применение машинного обучения в экономике
ID 1174057
Статус:
Маго-лего
Кто читает:
Департамент экономики
Где читается:
Санкт-Петербургская школа экономики и менеджмента
Когда читается:
1, 2 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Сысоев Дмитрий Сергеевич
Язык:
английский
Кредиты:
6
Контактные часы:
48
Course Syllabus
Abstract
The average course completion time may vary depending on the student's initial training. The prerequisite for mastering the course is knowledge of mathematics at the secondary education level and the basics of the Python programming language. Students' academic success is assessed through programming assignments in the form of contests, as well as written controls in the form of tests. The final exam is a contest with a presentation of the solutions proposed by the students. With the help of course assignments, basic methods of data preprocessing are worked out. model construction, interpretation of results. The course does not involve lectures, all theoretical materials are provided to students in practical classes.
Learning Objectives
- • Understanding the basic rules of syntax, data types, and built-in constructs • Create custom preprocessing pipelines • Mastering the main Python machine learning library: sklearn • Formation of basic skills in using Python as a classification and forecasting tool
Expected Learning Outcomes
- the student is able to explain the main types of data and the formulation of the research task
- the student is able to create basic machine learning models for regression and classification tasks
- the student is able to find and eliminate syntactic and logical errors in scripts
- the student is able to analyze the results obtained in order to describe economic processes
Course Contents
- 1. Problem statement, data, pipelines, metrics
- 2. Linear regression models
- 3. Classification: logistic regression, imbalance
- 4. Trees and ensembles for tabular data
- 5. Interpretation, stability, drift
- 6. Time series for economists
- 7. Automatic
Bibliography
Recommended Core Bibliography
- Machine learning : beginner's guide to machine learning, data mining, big data, artificial intelligence and neural networks, Trinity, L., 2019
Recommended Additional Bibliography
- Data mining : practical machine learning tools and techniques, Witten, I. H., 2011
- Text as Data: A New Framework for Machine Learning and the Social Sciences, Grimmer, J., 2022