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Part I AutoML Methods 1 Hyperparameter Optimization Matthias Feurer and Frank Hutter 2 Meta-Learning Joaquin Vanschoren 3 Neural Architecture Search .¡­. Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter Part II AutoML Systems 4 Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA.. Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown 5 Hyperopt-Sklearn. Brent Komer, James Bergstra, and Chris Eliasmith 6 Auto-sklearn: Efficient and Robust Automated Machine Learning Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, and Frank Hutter 7 Towards Automatically-Tuned Deep Neural Networks. Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, and Frank Hutter 8 TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning Randal S. Olson and Jason H. Moore 9 The Automatic Statistician Christian Steinruecken, Emma Smith, David Janz, James Lloyd, and Zoubin Ghahramani Part III AutoML Challenges 10 Analysis of the AutoML Challenge Series 2015-2018 ¡­. Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boull¨¦, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu, Damir Jajetic, Bisakha Ray, Mehreen Saeed, Mich¨¨le Sebag, Alexander Statnikov, Wei-Wei Tu, and Evelyne Viegas