自动机器学习（AutoML）是将机器学习应用于实际问题的过程的自动化过程。AutoML涵盖了从原始数据集到可部署的机器学习模型的完整管道。提出将AutoML作为基于人工智能的解决方案来应对不断增长的应用机器学习的挑战。 AutoML的高度自动化允许非专家使用机器学习模型和技术，而无需首先成为该领域的专家。 从机器学习角度讲，AutoML 可以看作是一个在给定数据和任务上学习和泛化能力非常强大的系统。但是它强调必须非常容易使用；从自动化角度讲，AutoML 则可以看作是设计一系列高级的控制系统去操作机器学习模型，使得模型可以自动化地学习到合适的参数和配置而无需人工干预。
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随着推荐任务的日益多样化和推荐模型的日益复杂，开发出一套能够很好地适应新的推荐任务的推荐系统变得越来越具有挑战性。在本教程中，我们将重点讨论自动机器学习(AutoML)技术如何有益于推荐系统的设计和使用。具体地说，我们将从一个完整的范围开始描述什么是可以自动推荐系统。然后，我们将在此范围内对特征工程、超参数优化/神经结构搜索和算法选择三个重要的主题进行详细阐述。将介绍、总结和讨论这些主题下的核心问题和最近的工作。
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We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at https://medmnist.github.io/.
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We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at https://medmnist.github.io/.
