Магистратура
2026/2027
Машинное обучение (углубленный курс)
Статус:
Курс по выбору (Статистический анализ в экономике)
Кто читает:
Департамент теоретической экономики
Где читается:
Факультет экономических наук
Когда читается:
1-й курс, 4 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Язык:
английский
Кредиты:
3
Контактные часы:
36
Course Syllabus
Abstract
The course describes main recent machine learning and data analysis methods as well as their application in economic research. Special attention in the course is paid to the implementation of these algorithms and models in Python
Learning Objectives
- Knowledge and understanding of machine learning topics such as Bayesian methods, deep learning, and reinforcement learning.
Expected Learning Outcomes
- Code a Logit regression from scratch, run a classic Logit regression in Python, know alternative Logit regressions.
- Know how to run and visualize a regression. Write an OLS regression from scratch.
- Understand how Logit fits into a broader family of classification methods.
- Know how to code iteration algorithms
- Understand embeddings, know how to fit simple language models.
- Understand basics of deep-learning architechture. Know how to fit simple models.
Course Contents
- Reinforced Learning 2.0
- Bayes Methods
- 2. Regressional and visual analysis
- 3. Logit
- 5. Classification
- 8. Decision Trees
- Natural Language Processing 2.0
- Deep Learning 2.0
Assessment Elements
- attendence
- hw1
- hw2
- written exam
- QuizzesSeveral (2 or 3) quizzes covering material explained during the lecture.
