Big Data Workshop: A Crash Course in Machine Learning For Architects Part I

Humanity has dramatically increased it’s ability to generate and retain astronomically large amounts of data in the last decade. Sooner or later you probably will be working on a project that will have a significant “Data Science” component to it. This is the first of a two-part introduction using machine learning techniques to solve data science questions using the R programming language.

This hand-on talk will be a crash-course into the fun world of Machine Learning (ML). The tutorial will both be a theoretical survey of common machine learning techniques as well a quick introduction to R and how to use it to solve some practical problems using ML. The talk will not make you an expert on machine learning tools but it will give you a solid feel for what can be done with them and help you understand what your data scientist can realistically do for your organization with off-the-libraries and the challenges she has to deal-with day-to-day.

This is the first of two sessions. In this session we will cover

• A brief introduction to R and the RStudio IDE
• A taxonomy of ML algorithms
• Linear regression from a ML perspective
• Logistic regression
• Support Vector Machines (SVM)
• Curse of Dimensionality
• Dimensionality Reduction: Principle Component Analysis