Automated Machine Learning: Methods, Systems, Challenges

Automated Machine Learning: Methods, Systems, Challenges
  • eBook:
    Automated Machine Learning: Methods, Systems, Challenges
  • Author:
    Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
  • Edition:
    1st ed. 2019 edition
  • Categories:
  • Data:
    July 10, 2019
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    219 pages
  • Format:

Book Description
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.


Part I AutoML Methods
Chapter 1. Hyperparameter Optimization
Chapter 2. Meta-Learning
Chapter 3. Neural Architecture Search

Part II AutoML Systems
Chapter 4. Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA
Chapter 5. Hyperopt-Sklearn
Chapter 6. Auto-sklearn: Efficient and Robust Automated Machine Learning
Chapter 7. Towards Automatically-Tuned Deep Neural Networks
Chapter 8. TPOT: A Tree-Based Pipeline Optimization Tool for AutomatingMachine Learning
Chapter 9. The Automatic Statistician

Part III AutoML Challenges
Chapter 10. Analysis of the AutoML Challenge Series 2015–2018

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