Though machine learners claim for potentially decades that their methods yield great performance for time series forecasting, until recently machine learning methods were not able to outperform even simple benchmarks in forecasting competitions, and did not play a role in practical applications. This has changed in the last 34 years, with methods being able to win several prestigious competitions. The models are now competitive as more series, and longer series due to higher sampling rates, are typically available. In this tutorial, we will briefly recap the history of the field of forecasting and its developments parallel to machine learning, and then discuss recent developments in the field, around learning across series with global models, Machine Learning methods such as recurrent neural networks, CNNs, and other models, and how they are now able to outperform traditional methods. We further will look into the intricacies of forecast evaluation, and into more advanced topics such as hierarchical forecasting and multivariate forecasting.