Syllabus for "Introduction to Machine Learning on Apache Spark"

ml-spark-syllabus

Machine Learning in Broad Strokes
+ Mathematical vs. Intuitive Basis for Machine Learning
+ Basic Machine Learning Tasks
  + Supervised Learning
  + Unsupervised Learning
  + Reinforcement Learning

Spark in Broad Strokes
+ Architecture of a Spark Application
+ [Hands-On]: Familiarization with the Spark Shell
+ Truncated Overview of Functional Programming
  + Resilient Distributed Datasets (RDDs)
  + Basic Operations on RDDs

Supervised Learning as Exemplified by Linear Regression
+ Intuition and Math behind Linear Regression
+ [Hands-On]: Using the MLLib Linear Regression Algorithm in Spark
+ Discussion of Strengths/Limitations, Extensions to More Complex Algorithms

Unsupervised Learning as Exemplified by k-Means Clustering
+ Intuition and Math behind k-Means
+ [Hands-On]: Using the MLLib k-Means Algorithm in Spark
+ Discussion of Strengths/Limitations, Extensions to More Complex Algorithms

Reinforcement Learning as Exemplified by Q-Learning
+ Intuition, Model, and Math behind Q-Learning
+ [Hands-On]: Implementing a Q-Learner from Scratch in Spark.
+ Discussion of Strength/Limitations, Extensions to More Complex Algorithms
Discussion of Development in Spark

Questions, Discussion, and Hacking