Current research addresses adversarial learning problems, transfer learning, data science, machine learning for … All published papers are freely available online. Learn about the latest advancements. Our research group focuses on how computer programs can learn from and understand data, and then make useful predictions based on it. His research interests are in machine learning, networking and communications, transportation, and smart grids. One focus of the Wolverton group is to use machine learning to learn more about materials and to create models that can be used to discover new materials. By leveraging on the strengths of both machine learning models and physics-based models, it is possible to transform the way data and models are used to improve predictions of water systems. Machine learning touches every aspect of Spotify’s business. The group comes together from many different departments to celebrate and promote the history of Machine Learning at the university. Our group focuses both on designing novel algorithms for such complex interconnected data and applications of these algorithms on real-world data. The Machine learning research and innovation group at American Family Insurance aims at providing innovative and efficient AI-and -data-science-inspired solutions to the enterprise. Apple machine learning teams are engaged in state of the art research in machine learning and artificial intelligence. We will also prioritize your learning and help point you in the right direction; but you need to … Machine Learning Research Group The group is interested in the development and application of innovative computational learning models for the solution of computational complex problems. Machine Learning Research (MLR) is a scholarly open access, peer-reviewed, and fully refereed journal. We have applied our techniques in contexts such as cybersecurity, personal integrity, intelligent cities, video surveillance, data science, citizen science, etc. 30.5.2015 Xuran Zhao has been appointed to an assistant professorship at Zhejiang University of Technology. The Machine and Deep Learning Research Interest Group is a forum for researching potential applications of Machine and Deep Learning in library science, including discussions, publications and outreach to the wider Library community. Our projects in machine learning are often motivated by applications in communication systems and networks, online services, and social networks. We have broad research interests across machine learning and its applications. This journal provides a unified forum for researchers and scientists to share the latest research and developments in all areas of machine learning. Our group works broadly on designing machine learning models for complex, relational, unstructured, and heterogeneous data. The Machine Learning Group (MLG), founded in 2004 by G. Bontempi, is a research unit of the Computer Science Department of the ULB (Université Libre de Bruxelles, Brussels, Belgium), Faculty of Sciences, currently co-headed by Prof. Gianluca Bontempi and Prof. Tom Lenaerts. Machine learning algorithms are designed to automatically extract new knowledge out of data. We do this by undertaking fundamental ML research. Two Postdoctoral Research Positions Available in the Machine Learning Group August 10, 2017, 3:45 pm We are seeking two highly creative and motivated Research Assistants/Associates to join the Machine Learning Group at the University of Cambridge. Research: Machine Learning Applying machine learning to chemistry problems has a rich history in the context of property prediction (i.e., the development of QSAR/QSPR models), but has only recently been extended to other aspects of organic synthesis. Our interests span theoretical foundations, optimization algorithms, and a variety of applications (vision, speech, healthcare, materials science, NLP, … Our group is interested in a broad range of theoretical aspects of machine learning as well as applications. Machine learning is the study of computational processes that find patterns and structure in data. His broad research interests include randomized algorithms for large-scale machine learning. Research Our work is focused on machine learning: the problem of automatically building models which explain observed systems and predict their future behavior. Journal of Machine Learning Research. Machine learning is the study of adaptive computational systems that improve their performance with experience.. The Machine Learning Research Group comprises several groupings of Faculty, Postdocs and Students. The Machine Learning Research Group at the Department of Computer Science, IT University of Copenhagen (ITU) does research in a wide area of topics including: statistical machine learning, deep learning, natural language processing, algorithmic game theory, algorithms for big data, automated planning, robotics and image analysis. The mission of the Machine Learning Research Group (MLRG) is to scale Machine Learning (ML) across Oracle. Our research is inter-disciplinary, as we leverage methods from statistics, optimization, and computer science, towards a better understanding of the design principles behind learning algorithms. Machine Learning Research Group Electronics and Communication Sciences Unit Indian Statistical Institute. We coordinate ML and AI research with labs and universities and explore how recent advances in the area could impact problems relevant to the insurance domain. UiT Machine Learning Group Pushing the frontier Powered by the cool Arctic air, and located at 70° north, the core strength of the Machine Learning Group at UiT The Arctic University of Norway is in basic research for advancing statistical machine learning & AI methodology to face the societal and industrial data-driven challenges of the future. JMLR has a commitment to rigorous yet rapid reviewing. MDLM – The Machine Learning and Data Management Group @ SBA Research Research Topics: Machine Learning (ML) offers exciting possibilities for innovative products and improvements of existing services. In 2018, Science news named him one of the Top-10 scientists under 40 to watch. Group’s contact person: Pascal Poupart Group members Shai Ben-David Dan Brown Bill Cowan Ali Ghodsi Jesse Hoey Gautam Kamath Kate Larson Pascal Poupart Overview Machine learning is an area of specialization of statistics crossed with computer science, most notably with such areas as computational statistics, scientific computation, data visualization and computational Leader of the group Machine Learning: Peter Grünwald. Much of the current excitement around machine learning is due to its impact in a broad range of applications. We are a group of researchers with shared interests in machine learning and cybernetics. Welcome to the homepage of the Machine Learning research group at the Institute of Computer Science, University of Tartu. The group is concerned with questions in the area of intelligent data analysis (IDA). These algorithms integrate insights from various fields, including … Machine Learning. Each member may have multiple local affiliations to sub-groups in the MLRG. DHI research and innovation Machine learning DHI is exploring the use of machine learning models to enhance capabilities for data and predictive analytics. MLRG members' research interests cover a wide range of ML work. Stanford Machine Learning Group Our mission is to significantly improve people's lives through our work in Artificial Intelligence. In the information age, IDA goes beyond the pure collection and organisation of data. Welcome to the Machine Learning Group (MLG). Welcome to the machine learning research group at Binghamton! Machine Learning at Cornell is a interdisciplinary learning and research group made up of over 30 Cornell University faculty and hundreds of involved students and alumni. News […] We developed multiple award winning forecasting technologies, based on statistical machine learning and other techniques. Its goal is to educate librarians on uses of the complex techniques of machine learning and to provide a space for critically thinking both about new … The UT Machine Learning Research Group focuses on applying both empirical and knowledge-based learning techniques to natural language processing, text mining, bioinformatics, recommender systems, inductive logic programming, knowledge and theory refinement, planning, and intelligent tutoring. Most applications can be summarized under the umbrella of computational sustainability, a strongly interdisciplinary research area that uses machine learning approaches to address sustainability challenges in Aotearoa and worldwide. Read More The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. We work on a variety of topics including machine learning, deep learning, computer security, computer vision, and biometric. 21 сентября 2020 года — Конференция Recent Advances in Machine Learning, Data Science, Intelligent Systems & Networking (MaDaIn 2020), проводимая в городе Дананг, Вьетнам 5—6 декабря, принимает статьи до 30 сентября. Our main research topics are supervised learning (classifier adaptation, calibration, evaluation, ensembles, uncertainty estimation) and applications of machine learning and deep learning for machine perception, autonomous driving, neuroscience, biology and health. ... talking to experts, or re-implementing research papers. The Machine Learning Research Group at UT Austin is led by Professor Raymond Mooney, and our research has explored a wide variety of issues in machine learning for over three decades.Our current research focus is natural language learning. We are a highly active group of researchers working on all aspects of machine learning. The group is a fusion of two former research groups from Aalto University, the Statistical Machine Learning and Bioinformatics group and the Bayesian Methodology group. Delphi Research Group (Epidemiological Forecasting) Epidemiological forecasting is critically needed for decision making by public health officials, commercial and non-commercial institutions, and the general public. Recognizing interrelationships and dependencies in the data is an important aspect, in particular, if no …