A benefit of using ensembles of decision tree methods like gradient boosting is that they can automatically provide estimates of feature importance from a trained predictive model. In this post you will discover how you can estimate the importance of features for a predictive modeling problem using the XGBoost library in Python. Feature Importance and Feature Selection With XGBoost in Python Photo by Keith Roper, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python. A benefit of using gradient boosting is that after the boosted trees are constructed, it is relatively straightforward to retrieve importance scores for each attribute.

Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis. You can read more about Prof. Rigollet's work and courses on his website.

Google DeepMindhas announced it is working on a project to improve treatment on head and neck cancers, its third major collaboration with the NHS. The London-based AI research arm of the online search firm is partnering with University College London Hospital in an attempt to improve the scans available for radiotherapists by using machine learning. The project will use anonymised scans from up to 700 former patients. Radiotherapy works by bombarding cancerous cells with radiation to kill them, while minimising damage to the healthy cells around them. Clinicians target the treatment through a process called "segmentation": literally drawing around different parts of the patient's anatomy on scans, letting the radiotherapy machines know which tissue to target and which tissue to leave.

Drive.ai uses deep learning to teach self-driving cars – and to give them a voice Drive.ai wants to help autonomous cars talk with the people around them Drive.ai AI-based startup Banihal helps find a perfect match with neuroscience-based recommendation ...

The New Artificial Intelligence Market by Aman Naimat, published by O'Reilly: There are only 1,500 companies in North America that are doing anything related to AI today, even using its narrow, task-based definition. That means less than one percent of all medium-to-large companies across all industries are adopting AI.

A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) has developed an artificial intelligence (AI) method, aimed at training computers to interpret pathology images. The team trained the computer to distinguish between cancerous tumor regions and normal regions based on a deep multi-layer convolutional network. In an objective evaluation in which researchers were given slides of lymph node cells and asked to determine whether or not they contained cancer, the team's automated diagnostic method proved accurate approximately 92 per cent of the time. One of the researchers, Aditya Khosla, said, "This nearly matched the success rate of a human pathologist, whose results were 96 percent accurate." "In our approach, we started with hundreds of training slides for which a pathologist has labeled regions of cancer and regions of normal cells," said Dayong Wang.

RAGE AI significantly extends the frontier of deep learning and machine intelligence technology from "natural language processing" to "natural language understanding." RAGE AI incorporates deep linguistic parsing and proprietary innovations to understand meaning in context, which makes its solutions completely transparent, auditable and flexible. The platform facilitates unsupervised to supervised learning and contains several innovations to support automated knowledge acquisition including pragmatic knowledge. RAGE AI is not a black box and does not rely on statistical patterns present in training data. Introduced in 2011, RAGE AI is an integral part of the broader RAGE Enterprise platform, a provider of all process orchestration and automation capabilities.

A treasure hunter went missing in the Rocky Mountains, and a computer algorithm found him ... Drive.ai wants to give self-driving cars more brainpower, personality Drive.ai

AI machines are racist & it's all our fault - report

Apple was already quite successful when Steve Job's gut told him that the iPhone would be a major success. But nobody could have predicted just how much the invention of the iPhone would transform the company and the world. That kind of intuition that Jobs displayed is crucial for business owners/leaders whether it's big or small. Google's AlphaGo computer displayed similar intuition recently when it defeated the world champion of the complex board game "Go." The programmers who created AlphaGo fed it every move of 150,000 "Go" matches to give it a feel for the game and the best strategies for achieving victory, and they were just getting started.