Master 2024/2025

Machine Learning (Advanced level)

Type: Elective course (Economics and Economic Policy)

Area of studies: Economics

Delivered by: Department of Theoretical Economics

Where: Faculty of Economic Sciences

When: 1 year, 4 module

Mode of studies: offline

Open to: students of all HSE University campuses

Instructors: Pasha Andreyanov

Master’s programme: Economics and Economic policy

Language: English

ECTS credits: 3

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

• Знание и понимание таких тем машинного обучения как: баесовские методы, глубокое обучение и обучение с подкреплением.

Expected Learning Outcomes

• 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

• Reinforced Learning
• Natural Language Processing
• Bayes Methods
• Causal
• Deep Learning

Assessment Elements

• written homeworks
• written exam
• Presence and participation in class

Interim Assessment

• 2024/2025 4th module
  0.25 * Presence and participation in class + 0.25 * written exam + 0.5 * written homeworks

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

Authors

• Andreianov Pavel Pavlovich