Large-scale machine learning 1000-319bBML

-Distributing computation to clusters of commodity machines and distributed file system.
-MapReduce model and basic algorithmic techniques for this model. Comparing of MapReduce algorithms and typical algorithms for typical problems (matrix multiplication, multi-way join, counting triangles in large graphs).
-Total vs elapsed communication cost. Skew and methods to deal with it.
-Spark and Resilient Distributed Dataset model.
-Spark SQL and its optimizations.
-Serialization of Big data and columnar formats.
-Managed cloud data warehouse.
-Algorithms for stream pressing.
-Distributing typical machine learning algorithms, e.g., linear regression, clustering, decision trees or neural networks.
-Neural networks in large scale (data parallelism, model paralelizm).
-Learned index structores.