Registration via LPIS
|Friday||10/14/22||10:00 AM - 01:00 PM||D3.0.222|
|Friday||10/21/22||10:00 AM - 01:00 PM||D3.0.222|
|Friday||10/28/22||10:00 AM - 01:00 PM||D3.0.222|
|Friday||11/04/22||10:00 AM - 01:00 PM||D3.0.222|
|Friday||11/11/22||10:00 AM - 01:00 PM||D3.0.222|
|Friday||11/18/22||10:00 AM - 01:00 PM||D3.0.222|
|Friday||11/25/22||10:00 AM - 01:00 PM||D3.0.222|
|Friday||12/02/22||10:00 AM - 01:00 PM||D3.0.222|
|Friday||12/09/22||10:00 AM - 01:00 PM||D3.0.222|
|Friday||12/16/22||10:00 AM - 01:00 PM||D3.0.222|
|Friday||12/23/22||10:00 AM - 01:00 PM||D3.0.222|
|Friday||01/13/23||10:00 AM - 01:00 PM||D3.0.222|
|Friday||01/20/23||10:00 AM - 01:00 PM||D3.0.222|
|Friday||01/27/23||10:00 AM - 01:00 PM||TC.1.01 OeNB|
This course is an introduction to Machine Learning and Data Science for graduate students of Economics. The field course (1636) will introduce you to concepts in Data Science which will be practiced in the seminar (1709) using R. Hence, participation in both classes is necessary. The focus of the classes is on practical applications of a wide range of useful methods within the field of Data Science.
Both field course and seminar will cover the following topics:
- Introduction and Organisation
- Data Wrangling
- Exploratory Data Analysis and Data Visualisation
- Introduction into Spatial Methods and Spatial Data Visualisation
- Regression and Classification Trees, Cross Validation
- Random Forest and Boosted Regression Trees
- (Spatial) Bayesian Model Averaging
- Clustering Algorithms
- Support Vector Machine and Naive Bayes
The aim of this course is to introduce the students to various methods and concepts in Data Science, applying them in the context of economics, as well as improve their programming skills. Upon finishing this class, students will have a “Data Science Toolkit” at their disposal.
Attendance is compulsory. Students can be absent during 2 units, beyond that a confirmation of severe reasons for absence is requested. Students cannot be deregistered from the course after achieving accomplishments in one of the course's components.
Programming skills on an intermediate level are required (e.g. in R or Python). Although this is an applied class, basic understanding of probability, statistics, linear algebra and calculus is necessary.