PPT-Lecture 16: Unsupervised Learning

from Text Padhraic Smyth Department of Computer Science University of California Irvine Outline General aspects of text mining Namedentity extraction questionanswering systems etc Unsupervised learning from text documents. Adam Coates. Stanford University. (Visiting Scholar: Indiana University, Bloomington). What do we want ML to do?. Given image, predict complex high-level patterns:. Object recognition. Detection. Segmentation. Quoc V. Le. Stanford University and Google. Purely supervised. Quoc V. . Le. Almost abandoned between 2000-2006. - . Overfitting. , slow, many local minima, gradient vanishing. In 2006, Hinton, et. al. proposed RBMs to . to Speech . EE 225D - . Audio Signal Processing in Humans and Machines. Oriol Vinyals. UC Berkeley. This is my biased view about deep learning and, more generally, machine learning past and current research!. Liu . ze. . yuan. May 15,2011. What purpose does . Markov Chain Monte-Carlo(MCMC) . serve in this chapter?. Quiz of the Chapter. 1 Introduction. 1.1Keywords. 1.2 Examples. 1.3 Structure discovery problem. via Brain simulations . Andrew . Ng. Stanford University. Adam Coates Quoc Le Honglak Lee Andrew Saxe Andrew Maas Chris Manning Jiquan Ngiam Richard Socher Will Zou . Thanks to:. ShaSha. . Xie. * Lei Chen. Microsoft ETS. 6/13/2013. Model Adaptation, Key to ASR Success. http://youtu.be/5FFRoYhTJQQ. Adaptation. Modern ASR systems are statistics-rich. Acoustic model (AM) uses GMM or DNN. CS539. Prof. Carolina Ruiz. Department of Computer Science . (CS). & Bioinformatics and Computational Biology (BCB) Program. & Data Science (DS) Program. WPI. Most figures and images in this presentation were obtained from Google Images. General Classification Concepts. Unsupervised Classifications. Learning Objectives. What is image classification. ?. W. hat are the three broad classification strategies?. What are the general steps required to classify images? . Adam Coates, . Honglak. Lee, Andrew Y. Ng. 2017/03/09. 1. Introduction. Feature learning/representation is a major topic . when processing unlabeled high-dimensional . data. For example, how to cluster images by recognizing the objects inside?. ShaSha. . Xie. * Lei Chen. Microsoft ETS. 6/13/2013. Model Adaptation, Key to ASR Success. http://youtu.be/5FFRoYhTJQQ. Adaptation. Modern ASR systems are statistics-rich. Acoustic model (AM) uses GMM or DNN. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand USDA Forest Service. Juliette Bateman (she/her). Remote Sensing Specialist/Trainer, . juliette.bateman@usda.gov. Soil Mapping and Classification in Google Earth Engine. Day 2:. Supervised and Unsupervised Classifications. FROM BIG DATA. Richard Holaj. Humor GENERATING . introduction. very hard . problem. . deep. . semantic. . understanding. . cultural. . contextual. . clues. . solutions. . using. . labelling.