In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as "machine learning"), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book. If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors' website.

An artificial intelligence (AI) device may sit and (perhaps) pass the national college entrance exam ("gaokao") in math in 2017, a tech company said Thursday. The AI test taker, part of a project by the Ministry of Science and Technology, was designed by Chengdu Zhun Xing Yun Xue Technology Co., Ltd. According to the plan, the AI will attend next year's gaokao math test, usually on June 7, along with millions of Chinese students. Like its human peers, it will be asked to complete a 150-point math test in two hours in a room without Internet access. Fu Hongguang, who leads the development team, said the key to passing the exam includes understanding the language and knowledge inference.

Amazon has hired Xerox PARC employee and artificial intelligence (AI) researcher Ashwin Ram to head up AI R&D for Alexa, the e-commerce behemoth's virtual assistant. Ram first tweeted about his appointment on Tuesday, and a spokesperson for Amazon has now confirmed the hire but declined to offer any further comment. The veteran computer science researcher worked at PARC for the last five years, most recently holding the role of Area Manager & Chief Innovation Officer, Interactive Intelligence & Augmented Social Cognition, according to LinkedIn, where he worked on apps and technology related to health and well-being. Excited to join @Amazon to lead AI r&d team for Alexa, the conversational agent that powers @AmazonEcho. Ram is also an adjunct professor for the College of Computing at Georgia Tech, where he ran the Cognitive Computing Lab for 8 years up until 2011. It was in that role that he led research in AI and cognitive science.

Welcome aboard the Starship Comet – a virtual spaceship in the smartphone game Cosmic Cadet, which asks players to complete six levels of interstellar challenges in 30 minutes. The game may look and feel like Angry Birds, but it is testing more than your ability to swipe and aim. It is a psychometric assessment, which its creators believe will revolutionise the recruitment industry. Measuring cognitive processes such as resilience and problem-solving, the game collects data on how job candidates instinctively respond to given situations, thereby helping employers gain a better understanding of how they would perform in the role and whether they are a good fit for the company. Cosmic Cadet is one of three games available for iPhone and Android users.

I am working with a team of students from Imperial University on a project, one of the things they want to implement is a Recommendation engine for their new Cortana Intelligence Service. We had a interesting call today about some of their initial ideas and suggestions and I introduced them to the Azure Machine Learning Recommendations API. The recommendation engine is actually utilised by Microsoft Channel 9 online learning resource. So when you watch a video on channel 9 the recommendations are coming from a model built by this service. The model has been learning Channel 9 usage data back dated to July of 2014.

Abstract: We present a new algorithm for the contextual bandit learning problem, where the learner repeatedly takes one of K actions in response to the observed context, and observes the reward only for that chosen action. Our method assumes access to an oracle for solving fully supervised cost-sensitive classification problems and achieves the statistically optimal regret guarantee with only O ( p KT / log N) oracle calls across all T rounds, where N is the number of policies in the policy class we compete against. By doing so, we obtain the most practical contextual bandit learning algorithm amongst approaches that work for general policy classes. We further conduct a proof-of-concept experiment which demonstrates the excellent computational and prediction performance of (an online variant of) our algorithm relative to several baselines.

This study suggests a new prediction model for chaotic time series inspired by the brain emotional learning of mammals. We describe the structure and function of this model, which is referred to as BELPM (Brain Emotional Learning-Based Prediction Model). Structurally, the model mimics the connection between the regions of the limbic system, and functionally it uses weighted k nearest neighbors to imitate the roles of those regions. The learning algorithm of BELPM is defined using steepest descent (SD) and the least square estimator (LSE). Two benchmark chaotic time series, Lorenz and Henon, have been used to evaluate the performance of BELPM. The obtained results have been compared with those of other prediction methods. The results show that BELPM has the capability to achieve a reasonable accuracy for long-term prediction of chaotic time series, using a limited amount of training data and a reasonably low computational time.

Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treatment is not controlled by a known mechanism, is sometimes available. Under some strong assumptions, observational data allows for the estimation of dose-response curves. Estimating such curves nonparametrically is hard: sample sizes for controlled interventions may be small, while in the observational case a large number of measured confounders may need to be marginalized. In this paper, we introduce a hierarchical Gaussian process prior that constructs a distribution over the dose-response curve by learning from observational data, and reshapes the distribution with a nonparametric affine transform learned from controlled interventions. This function composition from different sources is shown to speed-up learning, which we demonstrate with a thorough sensitivity analysis and an application to modeling the effect of therapy on cognitive skills of premature infants.

There are various offerings out there if you want to use machine learning in your analysis nowadays. Nick WIlson spent his internship at BigML comparing three SaaS Machine Learning Services (BigML, Prior Knowledge and Google Prediction API), with WEKA as a benchmark. He wrote a series of blog posts about his findings. In his final post he gives a summary of his work, with links to the different blog posts for details. He let me re-blog his summary here.

Arjun Pratap is founder and Chief Executive Officer at EdGE Networks. Fueled by the vision to build innovative, future-focused HR technology solutions – which re-engineer Human Resource Management to positively impact business outcomes – Arjun leads EdGE Networks to be a disruptor in the skill development space. Prior to EdGE Networks, Arjun worked with organizations such as SpeedERA Networks and Akamai Technologies, where he was responsible for building their India and international businesses. He also headed the sales function at Dexler Information Solutions to provide strategic direction in building the company. Arjun holds a post graduate degree in Information Systems and International Business, from The University of Sydney, Australia.