Machine Learning with Python for Economics and Econometrics
Data is now available faster, has greater coverage and scope, and includes new types of observations and measurements that previously were not available. Modern datasets have more complex structure than the traditional time-series, cross-sectional or panel data models.
This new development in data is challenging for economists, econometricians, and modelers as the traditional methods are no more suitable for analyzing huge quantities of unstructured data.
This course explores the intersection of machine learning and economics. The course will cover standard machine learning techniques such as supervised and unsupervised learning, statistical learning theory and nonparametric and Bayesian approaches. The course is fully hands-on. Participants will learn the essentials of the Python language and how to implement machine learning using Python, Keras, and TensorFlow.
By the end of the course the participants will have acquired detailed knowledge of and hands-on experience in:
- Python
- Keras
- Tensorflow
- Statistical Learning, Linear in Parameters Models
- Classification, Clustering, Resampling
- Regularization and Shrinking, Nonlinear Models
- Trees, Bagging, Boosting, Gradient Boosting, Support Vector Machines
- Neural Nets, Convolutional Neural Nets, Deep Learning
The course uses a practical and very intensive approach to machine learning. The participants will use Python to implement machine learning algorithms and methods relevant for economics and econometrics.