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. 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 . Aaron Crandall, 2015. What is Deep Learning?. Architectures with more mathematical . transformations from source to target. Sparse representations. Stacking based learning . approaches. Mor. e focus on handling unlabeled data. . Image by kirkh.deviantart.com. Aditya. . Khosla. and Joseph Lim. Today’s class. Part 1: Introduction to deep learning. What is deep learning?. Why deep learning?. Some common deep learning algorithms. with. Students. Carl S. Moore, Assistant Director . Carl.moore@temple.edu. Teaching and Learning Center. Temple University . Wood, D., Bruner, J. S., & Ross, G. (1976). The Role of Tutoring in Problem Solving*. Journal of child psychology and psychiatry, 17(2), 89-100.. Introductions . Name. Department/Program. If research, what are you working on.. Your favorite fruit.. How do you estimate P(. y|x. ) . Types of Learning. Supervised Learning. Unsupervised Learning. Semi-supervised Learning. 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:. 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. 1. Intelligent Systems (AI-2). Computer Science . cpsc422. , Lecture . 10. Sep, 29. , 2017. CPSC 422, Lecture 10. 2. Lecture Overview. Finish Reinforcement learning. Exploration vs. Exploitation. On-policy Learning (SARSA). . . . . . Professor of Biology . The Last lecture – . Key objective. Make the case that the . college lecture is still a major tool . in teaching and we should not be so quick to . abandon this academic approach . Learning What is learning? What are the types of learning? Why aren’t robots using neural networks all the time? They are like the brain, right? Where does learning go in our operational architecture? The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand FROM BIG DATA. Richard Holaj. Humor GENERATING . introduction. very hard . problem. . deep. . semantic. . understanding. . cultural. . contextual. . clues. . solutions. . using. . labelling.