PPT-Unsupervised Learning

Unsupervised Learning DSCI 415 Brant Deppa PhD Professor of Statistics amp Data Science Winona State University bdeppawinonaedu The Entire Course in One Day Course Topics Introduction to Unsupervised Learning. 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 . Temporal Commonality Discovery. Wen-Sheng . Chu. , . Feng. Zhou and Fernando De la Torre. Robotics Institute, Carnegie Mellon University. July 9, . 2013. 1. Unsupervised Commonality Discovery. in . Images. 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!. . 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. . VATS lobectomy consultant mentoring. Leads: Tom Routledge, Mike Shackcloth. Background. UK VATS lobectomy uptake remains patchy. Increasing evidence that it is standard of care for early stage lung cancer. Face Alignment . by Robust . Nonrigid. Mapping. Related Work. Supervised . Face Alignment . Active appearance models, T. . Cootes. et al. TPAMI’01.. Generalized shape regularization model, L. . Gu. Deep Auto-encoder. Unsupervised Learning. “We expect unsupervised learning to become far more important in the longer term. Human and animal learning is largely unsupervised: we discover the structure of the world by observing it, not by being told the name of every object.”. 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. Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections June 21 ACL 2011 Slav Petrov Google Research Dipanjan Das Carnegie Mellon University Part-of-Speech Tagging Portland has a thriving music scene . 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 Unsu. pervised . approaches . for . word sense disambiguation. Under the guidance of. Slides by. Arindam. . Chatterjee. &. Salil. Joshi. Prof. . Pushpak . Bhattacharyya. May 01, 2010. roadmap. Bird’s Eye View..