Магистратура
2020/2021
Применение машинного обучения в экономике
Статус:
Курс по выбору (Прикладная экономика и математические методы)
Направление:
38.04.01. Экономика
Кто читает:
Департамент экономики
Где читается:
Санкт-Петербургская школа экономики и менеджмента
Когда читается:
2-й курс, 2 модуль
Формат изучения:
без онлайн-курса
Преподаватели:
Дуплинский Артем Александрович
Прогр. обучения:
Прикладная экономика и математические методы
Язык:
английский
Кредиты:
5
Контактные часы:
28
Course Syllabus
Abstract
Using data to make predictions, test hypotheses and estimate models is an important skill on current job market. Many companies collect a lot of data and make their decisions data-driven. Machine learning disrupts many fields and promises to achieve superhuman performance in the coming decades. Statistical analysis allows to test hypothesis and verify which of the models fits the data the best. In this course we will cover different methods for supervised and unsupervised learning to develop a necessary toolkit for a successful data scientist. For some of the methods we will go into details to learn why and how they work. We will revisit concepts like stationarity, consistency, asymptotic normality. Moreover, we will touch on ethical implications of data science in the age of big data and apply learned methods to real business data sets.
Learning Objectives
- Students will feel comfortable orienting among different methods of machine learning and develop a feeling of why these methods work and how to extend them.
Expected Learning Outcomes
- Understand different methods for supervised learning such as linear regression, logistic regression, classification tools
- Understand different methods for unsupervised learning such as principal component analysis, k-means clustering
- Understand the concept of data generating process and how it is different to the concept of model
- Learn more details on hypotheses testing and concepts like stationarity, and ergodicity
Course Contents
- Opening and Intro to TS concepts
- Probability Models and Data Generating Processes
- Practical differences between machine learning and statistical approaches
- Presentations and Questions
Interim Assessment
- Interim assessment (2 module)0.09 * assignment 1 + 0.09 * assignment 2 + 0.7 * Exam + 0.12 * Presentation
Bibliography
Recommended Core Bibliography
- Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting (Vol. 2nd ed). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=108031
Recommended Additional Bibliography
- Ragnar Nymoen. (2019). Dynamic Econometrics for Empirical Macroeconomic Modelling. World Scientific Publishing Co. Pte. Ltd. https://doi.org/10.1142/11479