You need standard datasets to practice machine learning. In this short post you will discover how you can load standard classification and regression datasets in R. This post will show you 3 R libraries that you can use to load standard datasets and 10 specific datasets that you can use for machine learning in R. It is invaluable to load standard datasets in R so that you can test, practice and experiment with machine learning techniques and improve your skill with the platform. There are hundreds of standard test datasets that you can use to practice and get better at machine learning. Most of them are hosted for free on the UCI Machine Learning Repository.

Keras is a deep learning library that wraps the efficient numerical libraries Theano and TensorFlow. In this post you will discover how to develop and evaluate neural network models using Keras for a regression problem. Regression Tutorial with Keras Deep Learning Library in Python Photo by Salim Fadhley, some rights reserved. The problem that we will look at in this tutorial is the Boston house price dataset. You can download this dataset and save it to your current working directly with the file name housing.csv.

Students and faculty in several of Colorado State University's online programs will begin using "intelligent tutoring" technology in courses this fall. This comes as CSU Online announced last week a new partnership with Cognii, Inc., a leading provider of Artificial Intelligence-based educational technology. CSU faculty and instructional designers will work with Cognii to develop learning and assessment tools powered by Cognii's Virtual Learning Assistant, which is designed to improve students' learning outcomes, increase instructors' productivity, and enable high-quality personalized education at a large scale. "The use of Cognii in the classroom is expected to improve learning outcomes, turning assessment into learning while enhancing the effectiveness of the time our faculty devote to teaching," said Mike Palmquist, CSU's Associate Provost for Instructional Innovation. "Through this partnership, CSU is on the cutting edge of recent research and innovation in the fields of natural language processing, cognitive sciences, and machine learning, and an example of how the University is taking bold steps toward transforming access to quality education."

As readers of my blog will know, I want to learn more about machine learning. I've managed to run some samples and I've built my own first little samples. It feels like the next step is to understand more about the different algorithms, for example when to pick which one and how to tune the parameters to achieve the best results. To learn more, I've started to watch the first hours of the awesome courses below. The courses are a great introduction to machine learning and very different from most other videos I found which often seem to assume you are already a data scientist.

We've been using TensorFlow in daily research and engineering since it was released almost six months ago. We've learned a lot of things along the way. Because there are many subjective articles on TensorFlow and not enough helpful documentation, I've sprinkled in examples, tutorials, docs, and code snippets wherever possible. When it comes to machine learning, it is easy to focus on the tech (features, capabilities, benchmarks, etc). But good programmers know it is much harder to write code that humans will use, versus code that a machine can compile and execute.

In a blog post titled "Preparing for the Future of Artificial Intelligence," Deputy U.S. CTO Ed Felten announced the creation of the Machine Learning and Artificial Intelligence Subcommittee and a series of workshops to encourage public discourse about the emerging technology. "There are tremendous opportunities and an array of considerations across the Federal Government in privacy, security, regulation, law, and research and development to be taken into account when effectively integrating this technology into both government and private-sector activities," wrote Felten. Related: Google's newly launched Magenta Project aims to create art with artificial intelligence Open to the public, the workshops are co-hosted by the White House Office of Science and Technology and National Economic Council with academic and non-profit organizations across the country. The goal is to "identify challenges and opportunities" related to AI, which will lead to a public report by the subcommittee later this year. The first event was co-hosted by the University of Washington's School of Law in Seattle on May 24 and considered AI as applied to law and governance.

This week we completed four in-depth training webinars focused on WhizzML, BigML's new domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and easily sharing them with others. We already have our first batch of WhizzML graduates merely a week after launch. However, many of you were either not able to secure a live webinar spot or not able to join us at the scheduled date and time. Don't fret if you missed any of these training sessions. You can now watch the whole series at your own pace on BigML's YouTube channel.

As spatial query systems such as EQS (Unreal Engine 4), TPS (CryEngine), and PQS (Luminous Studio) have matured, auto-generated spatial queries are increasingly relied upon for robust dynamic position selection. We present a series of techniques and extensions to these systems used by Square Enix to produce novel behaviors and improve position selection in our current generation of AAA RPG titles. In addition, we have expanded UE4's Environment Query System to serve as a general-purpose utility system; we show how minor modifications allowed the team to use EQS to coordinate combat, reduce behavior tree complexity with a hybrid BT/US approach, and increase character AI quality for a range of tasks such as action and target selection. Attendees will learn how to get the most out of modern spatial query systems with a combination of new techniques and best practices to maximize quality and extend their application to new areas.

When it comes to robots replacing humans, we might think we have the upper hand since we're the ones who build and program them but that's not neccesarily the case anymore. Google is taking a different approach to training its robots – it's letting them teach each other. Researchers at Google have released a report showing how they connected 14 robotic arms together and used convolutional neural networks to let them teach themselves how to pick things up. The approach mimics how young children learn between the ages of one and four years old, and is essentially helping the robots to develop reliable hand-eye coordination. Typically, a robot would be programmed to carry out specific tasks, but this method shows how they can learn through trial-and-error in combination with a neural network – the same way a child learns how to do something by watching other people.

For complex, high-dimensional Markov Decision Processes (MDPs), it may be necessary to represent the policy with function approximation. A problem is misspecified whenever, the representation cannot express any policy with acceptable performance. We introduce IHOMP : an approach for solving misspecified problems. IHOMP iteratively learns a set of context specialized options and combines these options to solve an otherwise misspecified problem. Our main contribution is proving that IHOMP enjoys theoretical convergence guarantees. In addition, we extend IHOMP to exploit Option Interruption (OI) enabling it to decide where the learned options can be reused. Our experiments demonstrate that IHOMP can find near-optimal solutions to otherwise misspecified problems and that OI can further improve the solutions.