课程介绍

Apache SystemML is a declarative style language designed for large-scale machine learning. It provides automatic generation of optimized runtime plans ranging from single-node, to in-memory, to distributed computations on Apache Hadoop and Apache Spark. SystemML algorithms are expressed in R-like or Python-like syntax that includes linear algebra primitives, statistical functions and ML-specific constructs.

As a data scientist, engineer, or just a fellow interested in machine learning, your productivity will increase while having the flexibility to express custom analytics and not worry about the underlying optimization engine. Automatic scalability and optimization is handled by SystemML. This course will not only provide you with a view of how the optimizers function but also provide hands-on examples of ML algorithms and how to run them.

课程大纲

考核标准

课件浏览100%,客观练习0%,主观练习0%,课内讨论0%。
课程内容不断迭代，成绩以当时的课程内容为准，一旦合格，可以申请证书。申请证书后，以结课处理，成绩不再改动