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Machine Learning in Economics

2020/2021
Учебный год
ENG
Обучение ведется на английском языке
5
Кредиты
Статус:
Курс по выбору
Когда читается:
2-й курс, 2 модуль

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

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

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

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
Assessment Elements

Assessment Elements

  • non-blocking assignment 1
  • non-blocking Exam
  • non-blocking Presentation
  • non-blocking assignment 2
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.09 * assignment 1 + 0.09 * assignment 2 + 0.7 * Exam + 0.12 * Presentation