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Course 2022-2023 a.y.

30605 - AI APPLICATIONS IN ECONOMICS

Department of Economics

Course taught in English

Go to class group/s: 31

CLEAM (3 credits - I sem. - OP  |  SECS-P/01) - CLEF (3 credits - I sem. - OP  |  SECS-P/01) - CLEACC (3 credits - I sem. - OP  |  SECS-P/01) - BESS-CLES (3 credits - I sem. - OP  |  SECS-P/01) - WBB (3 credits - I sem. - OP  |  SECS-P/01) - BIEF (3 credits - I sem. - OP  |  SECS-P/01) - BIEM (3 credits - I sem. - OP  |  SECS-P/01) - BIG (3 credits - I sem. - OP  |  SECS-P/01) - BEMACS (3 credits - I sem. - OP  |  SECS-P/01) - BAI (3 credits - I sem. - OP  |  SECS-P/01)
Course Director:
CARLO RASMUS SCHWARZ

Classes: 31 (I sem.)
Instructors:
Class 31: CARLO RASMUS SCHWARZ


Suggested background knowledge

For BAI students, no background knowledge in addition to the courses of the first two years is required. For non-BAI students, some basic knowledge of machine learning is advantageous.


Mission & Content Summary
MISSION

The course aims to introduce students to state-of-the-art applications of machine learning in economics. The course will discuss how economists have incorporated machine learning techniques to tackle novel research questions. The course will center around the discussion of recent empirical economics papers that have employed machine learning models. At the end of the course, students should have obtained a better understanding of how machine learning can be applied to real-world settings and which new insights can be generated with it. The course also highlights common challenges that arise when machine learning models are applied to social science research.

CONTENT SUMMARY

1.       Introduction to machine learning in economics and social sciences

2.       Natural Language Processing

3.       Causal Machine Learning

4.       Applications of supervised machine learning

  • Measuring media bias 

 

5.       Applications of unsupervised machine learning

  • Measuring Ideology

 

6.       Applications of topic models

  • Measuring central bank communication

 

7.       Applications of word embeddings

  • Measuring biases in text

 

8.       Applications of image classification

  • Measuring characteristics of images

Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...

·         Explain the how machine learning can be used to answer new questions in the social sciences

·         Describe different machine learning methods and their application

·         Recognize potential applications of machine learning in real-world data

·         Illustrate the advantages and disadvantages of different machine learning methods in various contexts

APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...

·         Analyze the results of state-of-the-art economic research

·         Evaluate the validity of machine learning application

·         Assess how sensitive results are to the choice of machine learning algorithm

·         Critically weigh different machine learning algorithms against each other

·         Discuss how machine learning is broadening the fields of social science research.

·         Hypothesize about future applications of machine learning 


Teaching methods
  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Group assignments
DETAILS

The learning experience of this course includes face-to-face lectures by the instructors. The instructor will additionally provide some coding examples and exercises. In addition, students are also asked to prepare one group presentation on a research paper. These presentations are used to introduce students to the critical evaluation of empirical research and the contribution of papers to the scientific literature. Further, the presentations are intended to stimulate discussions about potential machine learning applications to social science questions. This will allow the students to develop their own ideas about future machine learning projects.


Assessment methods
  Continuous assessment Partial exams General exam
  • Group assignment (report, exercise, presentation, project work etc.)
  •     x
    ATTENDING AND NOT ATTENDING STUDENTS

    The course assessment takes place on a “pass or fail” bases. The student presentation will be used to assess the above-mentioned learning outcomes. The student presentation will ask students to deeply involve themselves with state-of-the-art economic research that uses machine learning and provide a presentation to their fellow students. In this way the presentation aims to assess if students are able:

    • To apply the knowledge and skills from the class to a new research paper
    • To discuss the validity of chosen machine learning approaches
    • To describe how machine learning helps to answer the relevant research question.
    • To articulate shortcomings of the chosen machine learning strategies
    • To suggest future applications of similar machine learning strategies

    For non-attending students the assessment is based on a written report they will be asked to write about one research paper.


    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS

    Teaching materials are announced before the start of the course and indicated or uploaded to the Bboard platform.

    Last change 21/06/2022 08:13