Advanced Distributed Machine Learning with Spark

开始时间: 04/22/2022 持续时间: 4 weeks

所在平台: EdxArchive 课程类别: 其他类别 大学或机构: UC BerkeleyX（加州大学伯克利分校） 授课老师： Ameet Talwalkar Jon Bates

课程主页: https://www.edx.org/archive/advanced-distributed-machine-learning-uc-berkeleyx-cs125x

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课程详情

Building on the core ideas presented in Distributed Machine Learning with Spark, this course covers advanced topics for training and deploying large-scale learning pipelines. You will study state-of-the-art distributed algorithms for collaborative filtering, ensemble methods (e.g., random forests), clustering and topic modeling, with a focus on model parallelism and the crucial tradeoffs between computation and communication.

After completing this course, you will have a thorough understanding of the statistical and algorithmic principles required to develop and deploy distributed machine learning pipelines. You will further have the expertise to write efficient and scalable code in Spark, using MLlib and the spark.ml package in particular.

课程大纲

• Training and deploying large-scale learning pipelines for various supervised and unsupervised settings
• Model parallelism and tradeoffs between computation and communication in distributed settings
• Collaborative filtering, decision trees, random forests, clustering, topic modeling, hyperparameter tuning
• Application of these principles using Spark, focusing on the spark.ml package

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