Применение машинного обучения в экономике

2024/2025

Статус: Маго-лего

Кто читает: Департамент экономики

Когда читается: 1, 2 модуль

Охват аудитории: для своего кампуса

Преподаватели: Сысоев Дмитрий Сергеевич

Язык: русский

Кредиты: 6

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Аннотация

Using data to make predictions, test hypotheses and estimate models is an important skill in the current job market. Many companies collect a lot of data and their decisions data-driven. Machine learning disrupts many fields and promises to achieve superhuman performance in the coming decades. Statistical analysis allows to test hypotheses and verify which of the models fits the data best. In this course we will cover different methods for supervised and unsupervised learning to develop a necessary toolkit for successful data scientists. For some of the methods we will go into details to learn why and how they work. Also we will touch on ethical implications of data science in the age of big data and apply learned methods to real business data sets.

Цель освоения дисциплины

Students will free comfortable orienting among different methods of machine learning and develop a feeling of why these methods work and to extend them

Планируемые результаты обучения

Understand the concept of data generating process and how it is different to the concept of model
Learn more details on hypothesis testing
Understand different methods for supervised learning such as regressions, random forest, gradient boosting etc.