About Brandon Rohrer
Brandon Rohrer is Principal Data Scientist at iRobot, specializing in predictive modeling of complex systems, algorithm design, and general purpose machine learning.
Brandon Rohrer Posts (18)

How Bayesian Inference Works  15 Nov 2016
Bayesian inference isn’t magic or mystical; the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Read an indepth overview here.

How Convolutional Neural Networks Work  31 Aug 2016
Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.

Data Science for Beginners 2: Is your data ready?  28 Jul 2016
This second video and writeup in the Data Science for Beginners series discusses what is required of your data before it can be useful.

Data Science for Beginners 1: The 5 questions data science answers  26 Jul 2016
A series of videos and writeups covering the basics of data science for beginners. This first video is about the kinds of questions that data science can answer.

A Pocket Guide to Data Science  11 Apr 2016
A pocket guide overview of how to get started doing data science, with a focus on the practical, and with concrete steps to take to get moving right away.

What questions can data science answer?  01 Jan 2016
There are only five questions machine learning can answer: Is this A or B? Is this weird? How much/how many? How is it organized? What should I do next? We examine these questions in detail and what it implies for data science.

5 Criteria To Determine If Your Data Is Ready For Serious Data Science  21 Dec 2015
If your data is a large, relevant, accurate, connected, and you also have a sharp question, you ready to do some serious data science. If you’re weak on 12 points, don’t worry. But if most criteria are not true, you need to do more preparation.

What Types of Questions Can Data Science Answer  29 Sep 2015
Data science has enabled us to solve complex and diverse problems by using machine learning and statistic algorithms. Here we have enumerated the common applications of supervised, unsupervised and reinforcement learning techniques