17-700 Machine Learning and Data Science at Scale
12 units, Carnegie Mellon University
Datasets are growing, new systems for managing, distributing, and streaming data are being developed, and new architectures for AI applications are emerging. This course will focus on techniques for managing and analyzing large datasets, and on new and emerging architectures for applications in machine learning and data science. Topics include machine learning algorithms and how they must be reformulated to run at scale on petabytes of data, as well as data management and cleaning techniques at scale. In addition to largescale aspects of data science and machine learning, this course will also cover core concepts of parallel and distributed computing and cloud computing, including handson experience with frameworks like Spark, streaming architectures like Flink or Spark Streaming, MLlib, TensorFlow, and more. The course will include programming assignments and a substantial final project requiring students to get handson experience with largescale machine learning pipelines or emerging computing architectures.
Prerequisites: 10-701 or 17-514 or 15-211 or 10-301 or 17-214 or 10-601 or 15-214
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