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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.
Daphne Koller is an Israeli-American computer scientist. She was a professor in the department of computer science at Stanford University and a MacArthur Foundation fellowship recipient. She is one of the founders of Coursera, an online education platform. Her general research area is artificial intelligence and its applications in the biomedical sciences. Koller was featured in a 2004 article by MIT Technology Review titled "10 Emerging Technologies That Will Change Your World" concerning the topic of Bayesian machine learning.
Sebastian Thrun is a German-American entrepreneur, educator, and computer scientist. He is CEO of Kitty Hawk Corporation, and chairman and co-founder of Udacity. Before that, he was a Google VP and Fellow, a Professor of Computer Science at Stanford University, and before that at Carnegie Mellon University. At Google, he founded Google X and Google's self-driving car team. He is also an adjunct professor at Stanford University and at Georgia Tech.
Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account. To do so, the predictions are modelled as a graphical model, which represents the presence of dependencies between the predictions. What kind of graph is used depends on the application. For example, in natural language processing, "linear chain" CRFs are popular, for which each prediction is dependent only on its immediate neighbours. In image processing, the graph typically connects locations to nearby and/or similar locations to enforce that they receive similar predictions.
Discriminative models, also referred to as conditional models, are a class of models frequently used for classification. They are typically used to solve binary classification problems, i.e. assign labels, such as pass/fail, win/lose, alive/dead or healthy/sick, to existing datapoints.
CALO was an artificial intelligence project that attempted to integrate numerous AI technologies into a cognitive assistant. CALO is an acronym for "Cognitive Assistant that Learns and Organizes". The name was inspired by the Latin word "Calo" which means "soldier's servant". The project started in May 2003 and ran for five years, ending in 2008.
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.
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The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research. It is supported by the International Machine Learning Society (IMLS). Precise dates vary year to year, but paper submissions are generally due at the end of January, and the conference is generally held the following July. The first ICML was held 1980 in Pittsburgh.
The AAAI Conference on Artificial Intelligence (AAAI) is a leading international academic conference in artificial intelligence held annually. It ranks 4th in terms of H5 Index in Google Scholar's list of top AI publications, after ICLR, NeurIPS, and ICML. It is supported by the Association for the Advancement of Artificial Intelligence. Precise dates vary from year to year, but paper submissions are generally due at the end of August to beginning of September, and the conference is generally held during the following February. The first AAAI was held in 1980 at Stanford University, Stanford California.
Structured prediction or structured output learning is an umbrella term for supervised machine learning techniques that involves predicting structured objects, rather than discrete or real values.
In statistics, a maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Markov models (HMMs) and maximum entropy (MaxEnt) models. An MEMM is a discriminative model that extends a standard maximum entropy classifier by assuming that the unknown values to be learnt are connected in a Markov chain rather than being conditionally independent of each other. MEMMs find applications in natural language processing, specifically in part-of-speech tagging and information extraction.
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In statistics, generalized iterative scaling (GIS) and improved iterative scaling (IIS) are two early algorithms used to fit log-linear models, notably multinomial logistic regression (MaxEnt) classifiers and extensions of it such as MaxEnt Markov models and conditional random fields. These algorithms have been largely surpassed by gradient-based methods such as L-BFGS and coordinate descent algorithms.
In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. Probabilistic classifiers provide classification that can be useful in its own right or when combining classifiers into ensembles.
John D. Lafferty is an American scientist, Professor at Yale University and leading researcher in machine learning. He is best known for proposing the Conditional Random Fields with Andrew McCallum and Fernando C.N. Pereira.
Eric Poe Xing is an American computer scientist whose research spans machine learning, computational biology, and statistical methodology. Xing is founding President of the world’s first artificial intelligence university, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
Dan Roth is the Eduardo D. Glandt Distinguished Professor of Computer and Information Science at the University of Pennsylvania and the Chief AI Scientist at Oracle. Until June 2024 Dan was a VP/Distinguished Scientist at AWS AI. In his role at AWS Roth led over the last three years the scientific effort behind the first-generation Generative AI products from AWS, including Titan Models, Amazon Q efforts, and Bedrock, from inception until they became generally available.
The following outline is provided as an overview of and topical guide to machine learning:
References
- ↑ "Bio for Andrew McCallum". Archived from the original on 4 June 2010. Retrieved 2010-05-31.
- ↑ "The Mathematics Genealogy Project - Andrew McCallum". Archived from the original on 6 May 2010. Retrieved 2010-05-31.
- ↑ "Faculty Directory". cs.umass.edu. Archived from the original on 4 June 2010. Retrieved 2010-05-31.
- ↑ "Elected AAAI Fellows" . Retrieved 2010-05-31.
- ↑ "AAAI Fellows Program".
- ↑ "All Award Winners". awards.acm.org. Retrieved 2017-12-22.
- ↑ "IMLS Board". machinelearning.org. Retrieved 19 March 2015.
President-Elect Andrew McCallum
- ↑ "UMass Center for Data Science Partners with Chan Zuckerberg Initiative to Accelerate Science and Medicine". umass.edu. Retrieved 2018-01-26.
- ↑ Lafferty, J.; McCallum, A.; Pereira, F.C.N. (2001). "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data". International Conference on Machine Learning. pp. 79–87.
- ↑ "Test-of-Time Award ICML'11". ICML. 2011. Retrieved 15 December 2014.
John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.
- ↑ "Google Scholar search for +mccallum +rainbow +software" . Retrieved 2010-05-31.
- ↑ "Rainbow". Archived from the original on 26 May 2010. Retrieved 2010-05-31.
- ↑ "FACTORIE" . Retrieved 2014-05-20.
- ↑ "OpenReview".
