Machine Learning For Architects II

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 or at least be expected to give input about building such system. This is the second 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) aimed at absolute beginners. 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 second of two sessions. In this session we will cover

• A brief introduction to R and the RStudio IDE
• Neural Networks
• Auto-Encoders
• K-Means Clustering
• Graphical Models
• Hidden-Markov Models
• Feature Generation
• Variance-Bias Trade-Off and Model Selection