Excerpt from course description
- EBA 3530
- 7.5 Credits
This course provides a thorough introduction to two central problems in applied analytics: causal analysis on one side, and machine learning and forecasting techniques on the other. The aims of the two problems are complementary, and are here presented together to emphasize their differences and connections.
- Fundamental principles of statistical learning and forecasting techniques: bias/variance trade-off, cross validation techniques and pseudo out of sample methods.
- The problems surrounding analysing causality through observational data.
- Experiments and quasi-experiments.
- Key Machine Learning algorithms, including, regression, time series processes, regularization, and classification, and their connection to the above issues.
This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.