It can be hard to know where to start when you want to learn about Bayesian statistics. I am frequently asked to share my favorite introductory resources to Bayesian statistics, and my go-to answer has been to share a dropbox folder with a bunch of PDFs that aren't really sorted or cohesive. In some sense I was acting as little more than a glorified Google Scholar search bar. It seems like there is some tension out there with regard to Bayes, in that many people want to know more about it, but when they pick up, say, Andrew Gelman and colleagues' Bayesian Data Analysis they get totally overwhelmed. And then they just think, "Screw this esoteric B.S." and give up because it doesn't seem like it is worth their time or effort.

Artificial Intelligence is just around the corner. Of course, it's been just around the corner for decades, but in part that's our own tendency to move the goalposts about what'intelligence' is. Once, playing chess was one of the smartest things you could do. Now that a computer can easily beat a Grand Master, we've reclassified it as just standard computation, not requiring proper thinking skills. With the rise of deep learning and the proliferation of machine learning analytics, we edge ever closer to the moment where a computer system will be able to accomplish anything and everything better than a human can. So should we start worrying about SkyNet?

No conflict of interest, all decisions based on information, rules and logic. Unfortunately it is not that easy. A machine compliance today or in the near future is a rules-based compliance. A complete new condition may lead to a situation that none of the defined rules can apply. Use the most similar known condition and apply its rules.

Last week we previewed this in "How Machine Learning APIs are Being Used to Predict Startup Success." Can there be a quantifiable way to hedge investors' risk and ensure they are betting on the right horse? According to the startup "jury" algorithm PreSeries, it's mathematically probable to predict which startup is most likely to succeed and that startup is Novelti. This startup which uses online machine-learning algorithms to convert Internet of Things sensor data into real-time intelligence, machine learning and pattern recognition was predicted to be successful with an 87 percent likelihood. Novelti beat out four other predictive analytics and artificial intelligence competitors--Intranetum, Emotion Research Lab, Datatrics and restb--at the PAPIs Connect conference for machine learning and predictive APIs.

In this tutorial we will discuss about Maximum Entropy text classifier, also known as MaxEnt classifier. The Max Entropy classifier is a discriminative classifier commonly used in Natural Language Processing, Speech and Information Retrieval problems.

Why do I keep using grids with my pathfinding tutorials? Because they're easy to use, and were incredibly common back when I started working on games. They work with graph-based pathfinding algorithms. I plan to build some demos of non-grid pathfinding graphs too, but grids are easier to explain. I typically use a list of edges per node, but you could also use a global set of edges, or a matrix representation that keeps track of which pairs of nodes have an edge between them.

Figuring out where to live is never easy. Do you settle in the house next to the elementary school or the one a few miles away that's cheaper? You decide to confide in your robotic assistant, who asks you a series of questions about what's most important to you: Nearby schools, bars, or parks? After some back-and-forth, it tells you your affordable dream home is two miles away from a reputable public school. Within the next decade, bots (in our phones and not) will be able to do that and more, says Andrew Moore, the dean of Carnegie Mellon's School of Computer Science.

This class is self paced. You can begin whenever you like and then follow your own pace. It's a good idea to set goals for yourself to make sure you stick with the course. Take a look at the "Class Summary," "What Should I Know," and "What Will I Learn" sections above. If you want to know more, just enroll in the course and start exploring.

Machine learning is becoming a buzzword, everybody talks aboit it and few seem to be interested in the math underneath (I find statements like "I wanted to know more but all sources were too statistical/mathematical and I wanted more practical stuff"). Let me tell you something: You can't really use Machine Learning if you don't know the statistical/mathematical basis. I am really upset when I see a Youtube video of some guy in T-Shirt probably working at a large organization ranting about Machine Learning and Data Science, telling programmers that maths is easy to grasp. Everybody knows how to press a button or, if you force me, almost everybody knows how to fix something in their Windows control panel, but that does not mean we can trust them when talking about building a secure payment system, Everybody can use Mahout or the like but that does not mean he knows jack about what he is doing using Naive Bayes to predict the class from thre variables (x, y, z) where z x 2 and x belongs to the range [-1,1]. Machine Learning is just a fancy word for the statistical/mathematical tools lying underneath, whose objective is to extract something that we may loosely call knowledge (or something that we understand) from data (or something chaotic that we do not understand), so that computers may take action based on the inferred knowledge.