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Directed Mixed Graph Models Ricardo Silva Statistical ScienceCSML University College London ricardostatsuclacuk Networks Processes and Causality Menorca 2012 Graphical Models Graphs provide a language for describing independence constraints. 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. Battiti. , Mauro . Brunato. .. The LION Way: Machine Learning . plus.  Intelligent Optimization. .. LIONlab. , University of Trento, Italy, . Apr 2015. http://intelligent-optimization.org/LIONbook. . Learning. for. . Word, Sense, Phrase, Document and Knowledge. Natural . Language Processing . Lab. , Tsinghua . University. Yu Zhao. , Xinxiong Chen, Yankai Lin, Yang Liu. Zhiyuan Liu. , Maosong Sun. . 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. Recap of Chapter 3. Mostly puzzle problems requiring little knowledge to solve.. “The simplicity we discovered there was largely a simplicity of process and a simplicity of the architecture of the mind.”. http://hunch.net/~mltf. John Langford. Microsoft Research. Machine Learning in the present. Get a large amount of labeled data . . where . . Learn a predictor . Use the predictor.. The Foundation: Samples + Representation + Optimization. Boyuan Chen. Outline. Definition and Motivation. Learning from Demonstration Pipeline. Paper Discussion. Recent and Future Work. Definition. Learning from Demonstration (LfD) is a paradigm for enabling robots to autonomously perform new tasks by learning from human teacher’s demonstration.. Workshop on Broad-Coverage . Semantic . Analysis. University of Amsterdam. With thanks to: . Collaborators:. . Ming-Wei . Chang. ,. . Chritos. Christodoulopoulos. , . Dan . Goldwasser. ,. . Dan Roth. University of Illinois, Urbana-Champaign. danr@illinois.edu. http://L2R.cs.uiuc.edu/~danr. 3322 SC. CS446: Machine Learning. What do you need to know:. . Theory of Computation. Probability Theory. Psychology 209. February . 1, 2013. The Concept of a Distributed Representation. Instead of assuming that an object (concept, etc) is represented in the mind by a single unit, we consider the possibility that it could be represented by patterns of activation over populations of units.. Today. Review your mock exams and make a target/plan for your revision. Look at key ways to increase marks in the exam. Media awards.. Coursework release sheets. Don’t forget: revision session today afterschool. IST597: Foundations of Deep Learning. The Pennsylvania State . University. Thanks to . Sargur. N. Srihari, . Rukshan. . Batuwita. , . Yoshua. . Bengio. Manual & Exhaustive Search. Manual Search. Topic 3. 4/15/2014. Huy V. Nguyen. 1. outline. Deep learning overview. Deep v. shallow architectures. Representation learning. Breakthroughs. Learning principle: greedy layer-wise training. Tera. . scale: data, model, . 1 NUIS and NUMED May 2013 1. Purpose The purpose of this paper is to outline how Newcastle University Students’ Union (NUSU) believes that student representation should work at Newcastle Univers