This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence. The ambiguities and noise inherent in human communication render traditional symbolic AI techniques ineffective for representing and analysing language data. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks.

This tutorial series shows how you can create a chatbot that can be deployed on two messaging applications: Facebook and Slack. In this final tutorial, I explain how you can enhance the chatbot by using IBM Watson Services. The news chatbot in this series uses developerWorks content as an example, but you can modify the content source to meet your own needs. In a previous tutorial, I described the importance of artificial intelligence (AI) in your chatbots and explained how it's hard to build your own AI--it requires not only rock stars in data science, but also a massive amount of data to train models. A small company typically does not have these kinds of resources.

Attend Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS and learn how to identify complex and dynamic patterns within your multilevel data. This advanced class provides a conceptual understanding of multilevel linear models (MLM) and multilevel generalized linear models (MGLM). Meet the Presenters Catherine Truxillo and Chris Daman discuss what you can expect to learn in this class. Attend a public course or enjoy the classroom experience right at your desktop, the choice is yours!

How someone might identify a dog. Important inputs that are given a lot of weight are highlighted in red. Notice how the neurons are organized into layers, where the further right the neurons are, the more abstract the input? In other words, the neurons on the left ask questions about general shapes and lines, whereas the neurons on the right ask questions about objects such as eyes or fur. Trained neural networks function in a very similar way, although they arrive at this conclusion after training with a lot of data.

Something is wrong in the way ML is being taught to developers. Most ML teachers like to explain how different learning algorithms work and spend tons of time on that. For a beginner who wants to start using ML, being able to choose an algorithm and set parameters looks like the #1 barrier to entry, and knowing how the different techniques work seems to be a key requirement to remove that barrier. Many practitioners argue however that you only need one technique to get started: random forests. Other techniques may sometimes outperform them, but in general, random forests are the most likely to perform best on a variety of problems (see Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?), which makes them more than enough for a developer just getting started with ML.

Machine learning gives you unimaginably powerful insights into data. Today, implementations of machine learning have been adopted throughout Industry and its concepts are numerous. This video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases.

Education has mostly followed the same structure for centuries -- e.g., the "sage on a stage" and "assembly line" models. As AI continues to disrupt industries like consumer electronics, ecommerce, media, transportation, and healthcare, is education the next big opportunity? Given that education is the foundation that prepares people to pursue advancements in all the other fields, it has the potential to be the most impactful application of AI. The three segments of the education market -- K-12, higher education, and corporate training -- are going through transitions. In the K-12 market, we are seeing the effect of the newer, more rigorous academic standards (Common Core, Next Generation Science Standards) shifting the focus toward measuring students' critical thinking and problem-solving skills and preparing them for college and career success in the 21st century.

Ajit Jaokar is a leading expert working at the intersection of Data Science, IoT, AI, Machine Learning, Big Data, Mobile, and Smart Cities. He teaches IoT and Data Science at Oxford and also is a director of Smart Cities Lab in Madrid. Ajit's work involves applying machine learning techniques to complex problems in the IoT and Telecoms domains. You can follow him on twitter @AjitJaokar and his blogs at Future Text. We are beyond thrilled to announce that Ajit will not only be speaking at our Big Data, Berlin meetup February 17, but he will also be at the head of the second workshop of our'Dataconomy Presents' series.

The 2012 publication Race against the Machine makes the case that the digitalization of work activities is proceeding so rapidly as to cause dislocations in the job market beyond anything previously experienced [1]. Unlike past mechanization/automation, which affected lower-skill blue-collar and white-collar work, today's information technology affects workers high in the education and skill distribution. Machines can substitute for brains as well as brawn. On one estimate, about 47% of total US employment is at risk of computerization [2]. If you doubt whether a robot or some other machine equipped with digital intelligence connected to the internet could outdo you or me in our work in the foreseeable future, consider news reports about an IBM program to "create" new food dishes (chefs beware), the battle between anesthesiologists and computer programs/robots that do their job much cheaper, and the coming version of Watson ("twice as powerful as the original") based on computers connected over the internet via IBM's Cloud [3]. On the darker side, you do not have to be paranoid to be paranoid about the potential technologies that the super-secret computers of the US National Security Agency (NSA) have on their digital drawing-boards.

We have conducted a study investigating the use of automated tutors for educating players in the context of serious gaming (i.e., game designed as a professional training tool). Historically, researchers and practitioners have developed automated tutors through a process of manually codifying domain knowledge and translating that into a human-interpretable format. This process is laborious and leaves much to be desired. Instead, we seek to apply novel machine learning techniques to, first, learn a model from domain experts' demonstrations how to solve such problems, and, second, use this model to teach novices how to think like experts. In this work, we present a study comparing the performance of an automated and a traditional, manually-constructed tutor. To our knowledge, this is the first investigation using learning from demonstration techniques to learn from experts and use that knowledge to teach novices.