test of neuroevoultion with cars that learn to drive a track Newest Video with Simplified NN : https://www.youtube.com/watch?v=DgRUyE7UJ3o Support: https://www.patreon.com/miorsoft Web: https://miorsoft.github.io/Site/index.html

Intelligent User Interfaces is where HCI meet Artificial Intelligence. Applications will be in interactive machine learning (iML).

Assignment 3 for the Machine Learning for Health Informatics course at TU Vienna - Anonymization using the SaNGreeA algorithm - GitHub - cassinius/mlhi-ass2-anonymization: Assignment 3 for the Mach...

The main idea behind this project is to bring in the Human into a machine learning algorithm. In this setup the Travelling salesman problem is chosen.

Human behavior is marked by a sophisticated ability to attribute outcomes and events to choices and experiences with surprising nuance. Understanding the mechanisms that govern this ability is a major focus for cognitive neuroscience. Reinforcement learning (RL) theory has provided a tractable

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.

'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's/graduate students in statistics and researchers in data-rich disciplines.

Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.

PyData London 2016 Probabilistic programming is a new paradigm that greatly increases the number of people who can successfully build statistical models and machine learning algorithms, and makes experts radically more effective. This talk will provide an overview of PyMC3, a new probabilistic programming package for Python featuring intuitive syntax and next-generation sampling algorithms. Machine learning is the driving force behind many recent revolutions in data science. Comprehensive libraries provide the data scientist with many turnkey algorithms that have very weak assumptions on the actual distribution of the data being modeled. While this blackbox property makes machine learning algorithms applicable to a wide range of problems, it also limits the amount of insight that can be gained by applying them. The field of statistics on the other hand often approaches problems individually and hand-tailors statistical models to specific problems. To perform inference on these models, however, is often mathematically very challenging, and thus requires time-deriving equations as well as simplifying assumptions (like the normality assumption) to make inference mathematically tractable. Probabilistic programming is a new programming paradigm that provides the best of both worlds and revolutionizes the field of machine learning. Recent methodological advances in sampling algorithms like Markov Chain Monte Carlo (MCMC), as well as huge increases in processing power, allow for almost complete automation of the inference process. Probabilistic programming thus greatly increases the number of people who can successfully build statistical models and machine learning algorithms, and makes experts radically more effective. Data scientists can create complex generative Bayesian models tailored to the structure of the data and specific problem at hand, but without the burden of mathematical tractability or limitations due to mathematical simplifications. This talk will provide an overview of PyMC3, a new probabilistic programming package for Python featuring intuitive syntax and next-generation sampling algorithms. ---- PyData is a gathering of users and developers of data analysis tools in Python. The goals are to provide Python enthusiasts a place to share ideas and learn from each other about how best to apply our language and tools to ever-evolving challenges in the vast realm of data management, processing, analytics, and visualization. We aim to be an accessible, community-driven conference, with tutorials for novices, advanced topical workshops for practitioners, and opportunities for package developers and users to meet in person. www.pydata.org Notebook: https://gist.github.com/anonymous/9287a213fe188a79d7d7774eef79ad4d Slides: https://docs.google.com/presentation/d/1QNxSjDHJbFL7vFwQHHheeGmBHEJAo39j28xdObFY6Eo/edit Twitter: https://twitter.com/twiecki 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Multi-armed bandit problems (MABPs) are a special type of optimal control problem well suited to model resource allocation under uncertainty in a wide variety of contexts. Since the first...

Senior Researcher, INRIA - Cited by 13,876 - Evolutionary Computation - Machine Learning

This paper investigates the control of an ML component within the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box optimization. The known CMA-ES weakness is its...

Image augmentation library in Python for machine learning. - GitHub - mdbloice/Augmentor: Image augmentation library in Python for machine learning.

Anaconda's open-source Distribution is the easiest way to perform Python/R data science and machine learning on a single machine.

The project on machine learning aims to stimulate a debate, increase awareness and demonstrate the potential of machine learning. Public views on machine learning surveyed by Ipsos Mori.

Welcome Students to the Holzinger Group, HCI-KDD, where machine learning meets health informatics. Science is to test crazy ideas, Engineering is to put these ideas into Business: The Holzinger group is doing theoretical, algorithmical, and experimental studies to help to understand the problem of knowledge extraction from complex data to discover unknown unknowns. We try to help to answer a grand question: How can we perform a task by exploiting knowledge extracted during problem solving of previous tasks. Contributions to this problem would have major impact to Artificial Intelligence generally, and Machine Learning specifically, as we could develop software which learns from previous experience similarly as we humans do. Andreas Holzinger is lead of the Holzinger Group, HCI–KDD, Institute for Medical Informatics, Statistics and Documentation at the Medical University Graz, and Associate Professor of Applied Computer Science at the Faculty of Computer Science and Biomedical Engineering at Graz University of Technology. Currently, Andreas is Visiting Professor for Machine Learning in Health Informatics at the Faculty of Informatics at Vienna University of Technology. He serves as consultant for the Canadian, US, UK, Swiss, French, Italian and Dutch governments, for the German Excellence Initiative, and as national expert in the European Commission. His research interests are in supporting human intelligence with machine intelligence to help solve problems in health informatics. Andreas obtained a PhD in Cognitive Science from Graz University in 1998 and his Habilitation (second PhD) in Computer Science from Graz University of Technology in 2003. Andreas was Visiting Professor in Berlin, Innsbruck, London (twice), and Aachen. He founded the Expert Network HCI–KDD to foster a synergistic combination of methodologies of two areas that offer ideal conditions toward unravelling problems in understanding intelligence: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with machine learning. Andreas is Associate Editor of Knowledge and Information Systems(KAIS), Section Editor of BMC Medical Informatics and Decision Making (MIDM), and member of IFIP WG 12.9 Computational Intelligence, more information: http://hci-kdd.org Visit the Cross Domain Conference for Machine Learning and Knowledge Extraction CD-MAKE https://cd-make.net/

AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important. On top of many AI applications already using...

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PARIS (Hollywood Reporter) - Long regarded as unfilmable, Patrick Suskind's 1985 novel "Perfume" has finally reached the screen in a blockbuster production that succeeds reasonably well in achieving what many said was beyond the scope of cinema: conveying the world of scent...