Studies on massive open online courses (MOOCs) users discuss the existence of typical profiles and their impact on the learning process of the students. However defining the typical behaviors as well as classifying the users accordingly is a difficult task. In this paper we suggest two methods to model MOOC users behaviour given their log data. We mold their behavior into a Markov Decision Process framework. We associate the user's intentions with the MDP reward and argue that this allows us to classify them.

The eCornell Machine Learning Certificate Program consists of 7 two-week courses aimed at developers, software and data engineers, data scientists and statisticians. Interested parties can take a pre-test to gauge their level of knowledge.

Online supermarket Ocado has announced plans to open a London tech hub that will create 260 digital jobs as it plans to bolster the talent behind its smart platform. The Ocado smart platform will be sold to companies across the world to help them move online, the company has said. The London office, which will be based in Old Street, becomes the company's sixth development centre, with others in Hatfield, Bulgaria, Spain and two in Poland. "We're excited to be expanding our network of development hubs into London, a city renowned for being at the forefront of tech innovation and home to many talented technologists," said Ocado Technology chief executive James Matthews "As we are growing rapidly, we are always on the lookout for people who share our drive to innovate," Matthews added. "This London hub joins our network of development centres to offer exciting employment opportunities for ambitious, tech-savvy individuals."

We attempt to make two arguments in this essay. First, through a case study of a mobile phone based voice-media service we have been running in rural central India for more than six years, we describe several implementation complexities we had to navigate towards realizing our intended vision of bringing social development through technology. Most of these complexities arose in the interface of our technology with society, and we argue that even other technology providers can create similar processes to manage this socio-technological interface and ensure intended outcomes from their technology use. We then build our second argument about how to ensure that the organizations behind both market driven technologies and those technologies that are adopted by the state, pay due attention towards responsibly managing the socio-technological interface of their innovations. We advocate for the technology engineers and researchers who work within these organizations, to take up the responsibility and ensure that their labour leads to making the world a better place especially for the poor and marginalized. We outline possible governance structures that can give more voice to the technology developers to push their organizations towards ensuring that responsible outcomes emerge from their technology. We note that the examples we use to build our arguments are limited to contemporary information and communication technology (ICT) platforms used directly by end-users to share content with one another, and hence our argument may not generalize to other ICTs in a straightforward manner.

We present Lumi\`ereNet, a simple, modular, and completely deep-learning based architecture that synthesizes, high quality, full-pose headshot lecture videos from instructor's new audio narration of any length. Unlike prior works, Lumi\`ereNet is entirely composed of trainable neural network modules to learn mapping functions from the audio to video through (intermediate) estimated pose-based compact and abstract latent codes. Our video demos are available at [22] and [23].

In this lecture introduction to generative adversarial networks (GANs) is carried out in detail. The primary focus of this lecture is on working and back-propagation process.

We discuss the objectives of any endeavor in creating artificial intelligence, AI, and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. This suggests that our attempts at AI could have been misguided; what we actually need to strive for can be termed artificial curiosity, AC, and intelligence happens as a consequence of those efforts. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. We discuss what these essential doctrines might be and why their establishment is required to form connections, possibly growing, between a knowledge store that has been built up and new pieces of information that curiosity will bring back. As more findings are acquired and more bonds are fermented, we need a way to, periodically, reduce the amount of data; in the sense, it is important to capture the critical characteristics of what has been accumulated or produce a summary of what has been gathered. We start with the intuition for this line of reasoning and formalize it with a series of models (and iterative improvements) that will be necessary to make the incubation of intelligence a reality. Our discussion provides conceptual modifications to the Turing Test and to Searle's Chinese room argument. We discuss the future implications for society as AI becomes an integral part of life.

Machine learning plays an increasing role in intelligent tutoring systems as both the amount of data available and specialization among students grow. Nowadays, these systems are frequently deployed on mobile applications. Users on such mobile education platforms are dynamic, frequently being added, accessing the application with varying levels of focus, and changing while using the service. The education material itself, on the other hand, is often static and is an exhaustible resource whose use in tasks such as problem recommendation must be optimized. The ability to update user models with respect to educational material in real-time is thus essential; however, existing approaches require time-consuming re-training of user features whenever new data is added. In this paper, we introduce a neural pedagogical agent for real-time user modeling in the task of predicting user response correctness, a central task for mobile education applications. Our model, inspired by work in natural language processing on sequence modeling and machine translation, updates user features in real-time via bidirectional recurrent neural networks with an attention mechanism over embedded question-response pairs. We experiment on the mobile education application SantaTOEIC, which has 559k users, 66M response data points as well as a set of 10k study problems each expert-annotated with topic tags and gathered since 2016. Our model outperforms existing approaches over several metrics in predicting user response correctness, notably out-performing other methods on new users without large question-response histories. Additionally, our attention mechanism and annotated tag set allow us to create an interpretable education platform, with a smart review system that addresses the aforementioned issue of varied user attention and problem exhaustion.

Advanced Machine Learning is a live, 8-week, part-time, three hours twice a week online bootcamp that offers a deeper understanding of machine learning techniques and how to handle unstructured data. In this course, you'll explore decision trees, neural networks, clustering, KMeans, time series, signal processing and more. At the end of this course, you'll be able to handle structured data and apply techniques to large volumes of real-world unstructured data to solve pressing business problems.

Join this VB Live webinar to learn about the five biggest mistakes companies make when they bring cutting-edge customer service technology to their workflows, and how to leap over these pitfalls and into real results. Most business leads are aware of the importance of AI, says Michael Butler, head of customer success at Ople, but often don't know how to get started – or if an investment in AI technology is the smartest route to stacking up real ROI. Previously, as director of global ecommerce at VMWare, he was relying entirely on his data science team, Butler says. The team consisted of about 35 people on staff full time, and the problem was that they were slow to produce models and results. For instance, coming up with a model to score customers most likely to buy a new release would take six weeks; when an anniversary sale came along, it would take another six weeks, starting from scratch each time. It should take a matter of days, if not hours, Ople thought.