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- Code:
- ECOM-455
- Field:
- Econometrics
- Targets:
- Master’s students Research Master's students
- Organiser:
- University of Helsinki - Economics
- Instructor:
- Mika Meitz
- Period:
- Period 1
- Format:
- Participation in teaching
- Method:
- Distance learning
- Enrollment:
In case of conflicting information consider the Sisu/Course/Moodle pages the primary source of information.
Aalto, Hanken and UH economics students can enroll through their home university’s SISU. Further instructions are available on the How to enroll? page, also for students from other universities.
If you would like to count the credits towards your degree, please check your curriculum or contact your supervisor or student services for guidance.
- To access the Moodle course area, use all the features and participate in the activities (assignments, discussions), you must have successfully registered for the course in Sisu and logged in with your UH user ID.
- For more information on how to activate your UH user ID and register for a Moodle course area, click here.
Content
The course provides an introduction to modern machine learning concepts and methods that are useful in econometrics. A selection of methods will be discussed from a range of topics such as: Classification, Clustering, Generative models, Matrix completion, Neural networks, Shrinkage, and Sparsity. We also familiarize ourselves with case studies where these methods have been used in the econometric literature. The focus is on understanding the formulation and theory of the methods, but examples of practical applications and programming will also be given.
Learning outcomes
After the course, the student should
- know certain basic concepts of machine learning
- be familiar with a selection of machine learning methods useful in econometrics
- be able to find and read appropriate machine learning literature (textbooks, scientific articles, etc.)
- be able to explain and report machine learning methods to their peers
- be able to critically follow empirical research that employs machine learning methods
- have readiness to employ these tools in empirical research and to implement them in some programming language
- have the basic knowledge for more advanced methodological and applied studies in machine learning methods in econometrics
In order to reach the goals, the course consists of lectures and a number of term papers.