A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions. Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so. This demand has given rise to a new research topic concerned with fitting machine learning models fully automatically - AutoML. This paper gives an overview of the state of the art in AutoML with a focus on practical applicability in a business context, and provides recent benchmark results on the most important AutoML algorithms.

Generative Adversarial Networks, or GANs for short, are a deep learning technique for training generative models. The study and application of GANs are only a few years old, yet the results achieved have been nothing short of remarkable. Because the field is so young, it can be challenging to know how to get started, what to focus on, and how to best use the available techniques. In this crash course, you will discover how you can get started and confidently develop deep learning Generative Adversarial Networks using Python in seven days. Note: This is a big and important post. You might want to bookmark it. How to Get Started With Generative Adversarial Networks (7-Day Mini-Course) Photo by Matthias Ripp, some rights reserved.

In this tutorial, you will learn how to perform video classification using Keras, Python, and Deep Learning. This tutorial will serve as an introduction to the concept of working with deep learning in a temporal nature, paving the way for when we discuss Long Short-term Memory networks (LSTMs) and eventually human activity recognition. To learn how to perform video classification with Keras and Deep learning, just keep reading! Videos can be understood as a series of individual images; and therefore, many deep learning practitioners would be quick to treat video classification as performing image classification a total of N times, where N is the total number of frames in a video. Video classification is more than just simple image classification -- with video we can typically make the assumption that subsequent frames in a video are correlated with respect to their semantic contents.

The Center for Clinical and Translational Science and the Department of Bioinformatics will be hosting an Artificial Intelligence in Human Health Workshop on Saturday, September 14, 2019 in James B050, 8:30 am – 12:00 pm. The overarching goal of the workshop is to bring together clinical and basic scientists to promote the use of AI research in human health. Light breakfast will be served.

The complete code to accompany this tutorial appears below.

Likewise, we admire the story of musicians, artists, writers and every creative human because of their personal struggles, how they overcome life's challenges and find inspiration from everything they've been through. That's something that can't be automated, even if we achieve the always-elusive general artificial intelligence. This article will be a tutorial on using neural style transfer (NST) learning to generate professional-looking artwork like the one above. NST has been around for a while and there are websites that perform all of the functions before you, however, it is very fun to play around and create your own images. NST is quite computationally intensive, so in this case, you are limited not by your imagination, but primarily by your computational resources.

Storytelling is fundamental to language, including culture, conversation and communication in their broadest senses. It thus emerges as an essential component of intelligent systems, including systems where natural language is not a primary focus or where we do not usually think of a story being involved. In this paper we explore the emergence of storytelling as a requirement in embodied conversational agents, including its role in educational and health interventions, as well as in a general-purpose computer interface for people with disabilities or other constraints that prevent the use of traditional keyboard and speech interfaces. We further present a characterization of storytelling as an inventive fleshing out of detail according to a particular personal perspective, and propose the DREAMT model to focus attention on the different layers that need to be present in a character-driven storytelling system. Most if not all aspects of the DREAMT model have arisen from or been explored in some aspect of our implemented research systems, but currently only at a primitive and relatively unintegrated level. However, this experience leads us to formalize and elaborate the DREAMT model mnemonically as follows: - Description/Dialogue/Definition/Denotation - Realization/Representation/Role - Explanation/Education/Entertainment - Actualization/Activation - Motivation/Modelling - Topicalization/Transformation

Generative adversarial networks (GANs) are a recently introduced class of generative models, designed to produce realistic samples. This tutorial is intended to be accessible to an audience who has no experience with GANs, and should prepare the audience to make original research contributions applying GANs or improving the core GAN algorithms. GANs are universal approximators of probability distributions. Such models generally have an intractable log-likelihood gradient, and require approximations such as Markov chain Monte Carlo or variational lower bounds to make learning feasible. GANs avoid using either of these classes of approximations.

Expert Speaker: Paul Burpo In this Expert Talk, you will learn how to architect a big data solution in Azure. Topics will include Azure Data Bricks, Azure Machine Learning Services, Data Factory, and Storage.

University of California, Irvine AI/Machine Learning Part-Time Instructor Recruitment Period Open date: February 22nd, 2019 Last review date: Friday, Mar 1, 2019 at 11:59pm (Pacific Time) Applications received after this date will be reviewed by the search committee if the position has not yet been filled. Final date: Saturday, Feb 22, 2020 at 11:59pm (Pacific Time) Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled. Description At the University of California Irvine's Department of Continuing Education - Technology Programs, our mission is to provide the best technical professional development courses online. We are laser focused on inspiring our students to learn new technical coding skills and shaping the future for their success. We are passionate about our education programs that support our students to fulfil their career goals and we are empowered to help thousands of people learn online every day.