Master
2026/2027
Machine Learning (Advanced level)
Type:
Elective course (Economics and Economic Policy)
Delivered by:
Department of Theoretical Economics
Where:
Faculty of Economic Sciences
When:
1 year, 4 module
Open to:
students of all HSE University campuses
Language:
English
ECTS credits:
3
Contact hours:
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.
- Знать основные идеи обучения с подкреплением: policy update, value update...
- Уметь написать коды value iteration, policy iteration, q-iteration...
- Знать и понимать генеративные модели текстового анализа
- Знать и понимать дискриминативные модели текстового анализа
- Уметь запустить простейшую нейронную сеть.
Course Contents
- 2. Regressional and visual analysis
- 3. Logit
- 5. Classification
- Reinforced Learning
- Natural Language Processing
- Bayes Methods
- Deep Learning
- 8. Decision Trees
Assessment Elements
- attendence
- hw1
- hw2
- written exam
- QuizzesSeveral (2 or 3) quizzes covering material explained during the lecture.
Interim Assessment
- 2026/2027 4th module0.15 * hw2 + 0.1 * attendence + 0.15 * hw1 + 0.4 * written exam + 0.2 * Quizzes
Bibliography
Recommended Core Bibliography
- A first course in machine learning, Rogers, S., 2012
- Data mining : practical machine learning tools and techniques, Witten, I. H., 2011
- Foundations of machine learning, Mohri, M., 2012
- Machine learning, Mitchell, T. M., 1997
Recommended Additional Bibliography
- Introduction to natural language processing, Eisenstein, J., 2019
- The handbook of computational linguistics and natural language processing, , 2013