This course covers basic concepts of Machine Learning and its application in economics. Students learn about linear and nonlinear estimators, including lasso, ridge, random forests and gradient boosting. Also basics of neural networks and deep learning are covered. Students will also apply the learned algorithm in data examples and projects. The course includes lab sessions with programming in Python. Earlier completion of Introduction to Econometrics and other quantitative modules is recommended.