Causality, Machine learning and Forecasting
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.
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