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
Zero fluff.