Michael Irwin Jordan is an American scientist, professor at the University of California, Berkeley, research scientist at the Inria Paris, and researcher in machine learning, statistics, and artificial intelligence.
Vasant G. Honavar is an Indian-American computer scientist, and artificial intelligence, machine learning, big data, data science, causal inference, knowledge representation, bioinformatics and health informatics researcher and professor.
A Markov logic network (MLN) is a probabilistic logic which applies the ideas of a Markov network to first-order logic, defining probability distributions on possible worlds on any given domain.
The following outline is provided as an overview of and topical guide to artificial intelligence:
John Alan Robinson was a philosopher, mathematician, and computer scientist. He was a professor emeritus at Syracuse University.
Michael Lederman Littman is a computer scientist, researcher, educator, and author. His research interests focus on reinforcement learning. He is currently a University Professor of Computer Science at Brown University, where he has taught since 2012.
Dieter Fox is a German-American roboticist and a Professor in the Department of Computer Science & Engineering at the University of Washington, Seattle. He is also the Senior Director of Robotics Research at Nvidia. He received his PhD in Computer Science at the University of Bonn in 1998 on mobile robot localization and navigation. He is most notable for his contributions to several fields including robotics, artificial intelligence, machine learning, and ubiquitous computing. Together with Wolfram Burgard and Sebastian Thrun he is a co-author of the book Probabilistic Robotics. He was elected an AAAI Fellow in 2011.
Manuela Maria Veloso is the Head of J.P. Morgan AI Research & Herbert A. Simon University Professor Emeritus in the School of Computer Science at Carnegie Mellon University, where she was previously Head of the Machine Learning Department. She served as president of Association for the Advancement of Artificial Intelligence (AAAI) until 2014, and the co-founder and a Past President of the RoboCup Federation. She is a fellow of AAAI, Institute of Electrical and Electronics Engineers (IEEE), American Association for the Advancement of Science (AAAS), and Association for Computing Machinery (ACM). She is an international expert in artificial intelligence and robotics.
Informatics is the study of computational systems. According to the ACM Europe Council and Informatics Europe, informatics is synonymous with computer science and computing as a profession, in which the central notion is transformation of information. In some cases, the term "informatics" may also be used with different meanings, e.g. in the context of social computing, or in context of library science.
Usama M. Fayyad is an American-Jordanian data scientist and co-founder of KDD conferences and ACM SIGKDD association for Knowledge Discovery and Data Mining. He is a speaker on Business Analytics, Data Mining, Data Science, and Big Data. He recently left his role as the Chief Data Officer at Barclays Bank.
Qiang Yang is the Chair Professor, Department Head of CSE, HKUST in Hong Kong and University New Bright Professor of Engineering and Chair Professor from 2015. He was the founding head of Noah's Ark Lab. He had taught at the University of Waterloo and Simon Fraser University. His research interests are data mining and artificial intelligence.
Ryszard S. Michalski was a Polish-American computer scientist. Michalski was Professor at George Mason University and a pioneer in the field of machine learning.
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World is a book by Pedro Domingos released in 2015. Domingos wrote the book in order to generate interest from people outside the field.
Thomas G. Dietterich is emeritus professor of computer science at Oregon State University. He is one of the pioneers of the field of machine learning. He served as executive editor of Machine Learning (journal) (1992–98) and helped co-found the Journal of Machine Learning Research. In response to the media's attention on the dangers of artificial intelligence, Dietterich has been quoted for an academic perspective to a broad range of media outlets including National Public Radio, Business Insider, Microsoft Research, CNET, and The Wall Street Journal.
The following outline is provided as an overview of and topical guide to machine learning:
Huan Liu is a Chinese-born computer scientist.
Cynthia Diane Rudin is an American computer scientist and statistician specializing in machine learning and known for her work in interpretable machine learning. She is the director of the Interpretable Machine Learning Lab at Duke University, where she is a professor of computer science, electrical and computer engineering, statistical science, and biostatistics and bioinformatics. In 2022, she won the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (AAAI) for her work on the importance of transparency for AI systems in high-risk domains.
In network theory, collective classification is the simultaneous prediction of the labels for multiple objects, where each label is predicted using information about the object's observed features, the observed features and labels of its neighbors, and the unobserved labels of its neighbors. Collective classification problems are defined in terms of networks of random variables, where the network structure determines the relationship between the random variables. Inference is performed on multiple random variables simultaneously, typically by propagating information between nodes in the network to perform approximate inference. Approaches that use collective classification can make use of relational information when performing inference. Examples of collective classification include predicting attributes of individuals in a social network, classifying webpages in the World Wide Web, and inferring the research area of a paper in a scientific publication dataset.
Himabindu "Hima" Lakkaraju is an Indian-American computer scientist who works on machine learning, artificial intelligence, algorithmic bias, and AI accountability. She is currently an Assistant Professor at the Harvard Business School and is also affiliated with the Department of Computer Science at Harvard University. Lakkaraju is known for her work on explainable machine learning. More broadly, her research focuses on developing machine learning models and algorithms that are interpretable, transparent, fair, and reliable. She also investigates the practical and ethical implications of deploying machine learning models in domains involving high-stakes decisions such as healthcare, criminal justice, business, and education. Lakkaraju was named as one of the world's top Innovators Under 35 by both Vanity Fair and the MIT Technology Review.
Yixin Chen is a computer scientist, academic, and author. He is a professor of computer science and engineering at Washington University in St. Louis.
