Topic 1: Introduction and Linguistics 1207


Title (link) Author Date Votes Error
LOLWUT Jordan Ho, Patrick Littell, Tom McCoy 2017
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Minimum Spelling Trees John de Nero 2015
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aw-TOM-uh-tuh Patrick Littell 2008
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The Dualization Game Daniel Lovsted 2015
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Transducing Runes Jordan Boyd-Graber 2014
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DAWG Breeds Patrick Littell 2016
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Lexicondensed Tom McCoy 2014
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Text-o-matic Daniel Lovsted 2017
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The Old Man the Boats Emily Bender 2015
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Title (link) Author Date Votes Error
Media Frames Corpus v2.0 Dallas Card, Amber E. Boydstun, Justin H. Gross, Philip Resnik, Noah A. Smith 2015
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WikiHow-Dataset mahnazkoupaee 2018
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An Update to Open Images - Now with Bounding-Boxes Vittorio Ferrari 2017
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Quora Duplicate Questions Corpus Quora 2017
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boxscore-data N/A 2019
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DEAPdataset: a dataset for emotion analysis using eeg, physiological and video signals S. Koelstra, C. Muehl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, I. Patras 2011
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IPython minibook, 2nd edition Cyrille Rossant 2015
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Dialogue Datasets Phylliida 2018
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Datasets used in the IPython Cookbook Cyrille Rossant 2013
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Webis-TLDR-17 Corpus Syed, Shahbaz; Voelske, Michael; Potthast, Martin; Stein, Benno 2017
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Datasets For Machine Learning Tine 2016
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A Survey of Available Corpora for Building Data-Driven Dialogue Systems Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau 2019
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Build It, Break It The Language Edition Emily M. Bender, Hal Daume III, Allyson Ettinger, Harita Kannan, Sudha Rao, Ephraim Rothschild 2017
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webis-tldr-17-corpus webis-de 2018
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BNF Grammars for SQL-92, SQL-99 and SQL-2003 Ron Savage, Jonathan Leffler 2017
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Title (link) Lecturer Date Votes Error
What Is Inf2a about? Shay Cohen 2017
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Introduction to Graphical Models Eric Xing 2014
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Decision Trees & Limits of Learning Furong Huang 2018
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Project discussion Brendan O’Connor 2015
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EM for GMM Ansaf Salleb-Aouissi 2014
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Mathematics for Machine Learning: Continuous Optimization Marc Peter Deisenroth, A Aldo Faisal, Cheng Soon Ong 2018
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Bayes Theorem Dragomir Radev 2018
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Application of Bayes Theorem Frank Keller 2016
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Optimizing inputs François Fleuret 2019
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Boosting John Paisley 2017
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Speech and Language Processing ETHZ 2013
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Bayesian Hyperparameter Optimization Roger Grosse 2018
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Lecture 4: Language Model Evaluation and Advanced methods Kai-Wei Chang 2016
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Machine Learning Resources Dragomir Radev 2018
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Expectation and Related Concepts Chebyshevs Theorem Frank Keller 2016
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Reference Resolution Ralph Grishman 2018
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Introduction Ethem Alpaydın 2014
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Basic Text Processing Regular Expressions Dan Jurafsky 2017
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Natural Language Processing Introduction Yejin Choi 2018
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Advanced Topics in MCMC Eric Xing 2014
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Introduction Ansaf Salleb-Aouissi 2014
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Introduction to Natural Language Processing Brendan O’Connor 2015
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Text Preprocessing and Regular Expressions Ruihong Huang 2017
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Design and Analysis of Machine Learning Experiments Ethem Alpaydın 2014
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Finite-State Programming Some Examples Jason Eisner 2012
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Probabilities 2 Dragomir Radev 2017
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Discrete Neural Turing Machines Joan Bruna 2016
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Training and Optimization Stephen Clark 2017
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Deeper into Deep Learning and Optimizations Efstratios Gavves 2018
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Finite-State Transducers Jacob Eisenstein 2014
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Kernel PCA/LDA Ricardo Gutierrez-Osuna 2013
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Machine Learning Theory II Tom M. Mitchell 2015
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Introduction to Applied Linear Algebra Stephen Boyd, Lieven Vandenberghe 2018
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Natural Language Processing Lecture 1 Noah Smith 2017
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Distributions Independence Frank Keller 2016
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Beyond CFG Parsing David R. Mortensen, Alan W. Black 2018
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Parsing with Context Free Grammars Nathan Schneider 2018
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Gradient, Subgradient and how they may affect your grade(ient) David Sontag 2016
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Natural Language Understanding Graham Neubig 2018
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Linguistics Basics Dragomir Radev 2018
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Introduction to Natural Language Processing Jinho D. Choi 2017
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Autograd François Fleuret 2019
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Variable Dimension Models: Reversible Jump MCMC Nicholas Zabaras 2017
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Probability Lecture #7 Andrew McCallum 2007
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Language and Complexity Dragomir Radev 2017
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Weaknesses of Probabilistic Context-Free Grammars Michael Collins, 2018
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Pattern Recognition and Machine Learning Chapter 2: Probability Distributions Christopher M. Bishop 2016
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Neural networks Conditional random elds - factors, sufcient statistics and linear CRF Hugo Larochelle 2017
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Naive Bayes Lu Wang 2017
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Collaborative Filtering John Paisley 2017
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Empirical Methods in Natural Language Processing Lecture 1 Philipp Koehn 2008
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Regular expressions and Kleene’s theorem Shay Cohen 2017
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Variational Inference: Loopy Belief Propagation Eric Xing 2014
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Decision Trees Furong Huang 2018
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Lecture 1: Course Introduction Brendan O’Connor 2015
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Regularization and optimization Sida Wang 2018
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Basic Concepts II Ansaf Salleb-Aouissi 2014
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Lecture 2: N-gram Kai-Wei Chang 2016
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Linear Algebra 2 Dragomir Radev 2018
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Entropy Rate, Mutual Information Frank Keller 2016
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Naive Bayes Dan Klein and Pieter Abbeel 2014
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Introduction to Natural Language Processing Jacob Eisenstein 2014
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Linear algebra and MATLAB Ricardo Gutierrez-Osuna 2013
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Linear Regression Matt Gormley 2016
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Representation and Inference for Natural Language Patrick Blackburn, Johan Bos 1999
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Machine learning III Percy Liang 2017
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Conditional Probability and Independence Bayes Theorem Frank Keller 2016
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Introduction Sida Wang 2018
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Parametric Models Ethem Alpaydın 2014
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Finite-State Methods Jason Eisner 2012
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Introduction Dragomir Radev 2017
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Lecture Introduction Dan Roth 2018
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Graph-Induced Structured Input-Output Methods Eric Xing 2014
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Class Logistics Dragomir Radev 2018
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Empirical Risk Minimization Chris Dyer 2015
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Multilinguality and morphology Greg Durrett 2018
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Implementation of Bayesian Regression and Variable Selection Nicholas Zabaras 2017
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Lecture 8: Variational inference Julia Hockenmaier 2013
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Morphology and Lexicon Dragomir Radev 2017
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Introduction to NLP Kai-Wei Chang 2017
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Course overview David R. Mortensen, Alan W. Black 2018
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Sparse coding Hugo Larochelle 2017
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A note about NLP tools NLTK Nathan Schneider 2018
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Generalization and Regularization Xavier Bresso 2017
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Information Theory, Multivariate Gaussian, MLE Estimation, Robbins-Monro algorithm Nicholas Zabaras 2017
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Basics of Morphology Jirka Hana 2011
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Types and Static Type Checking Shay Cohen 2017
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Super VIP Cheatsheet: Machine Learning Afshine Amidi and Shervine Amidi 2018
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Introduction to Machine Learning Furong Huang 2018
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Natural Language Processing Introduction Nathan Schneider 2018
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Maximum A Posteriori and Expectation Maximization Ansaf Salleb-Aouissi 2014
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Mathematics for Machine Learning: Probability and Distributions Marc Peter Deisenroth, A Aldo Faisal, Cheng Soon Ong 2018
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Methods used in NLP Dragomir Radev 2018
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Algorithms for Natural Language Processing Nathan Schneider 2018
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Visualizing the processing in the input François Fleuret 2019
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Machine Learning for Data Science John Paisley 2017
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Theory of Automata and Formal Languages ETHZ 2013
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Lecture 12: EM Algorithm Kai-Wei Chang 2016
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Probabilities 1 Dragomir Radev 2018
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Application of Bayes Theorem 2 Frank Keller 2016
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Verbs and Pronouns Ralph Grishman 2018
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Supervised Learning Razvan C. Bunescu 2017
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CS11-747 Neural Networks for NLP Intro Graham Neubig 2017
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Greedy Algorithm Yejin Choi 2018
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Approximate Inference: Markov Chain Monte Carlo (MCMC) Eric Xing 2014
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Machine Learning Ansaf Salleb-Aouissi 2014
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Coreference and Entity Resolution Brendan O’Connor 2015
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Course Overview Ruihong Huang 2017
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Python Roger Grosse 2018
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Syntactic Attributes Morphology, heads, gaps, etc. Jason Eisner 2012
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Probabilities 1 Dragomir Radev 2016
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GAN, MCMC, Self-Supervised Learning Joan Bruna 2016
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Introduction Jonathan Berant 2017
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Deep Learning on Graph-Structured Data Efstratios Gavves 2018
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Beyond Context-Free Grammars Jacob Eisenstein 2014
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Ensemble Learning Ricardo Gutierrez-Osuna 2013
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Bias, Variance and Error Tom M. Mitchell 2015
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MIT Probability Course Summarized Zaid Alyafeai 2018
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Overview Percy Liang 2017
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Special Probability Distributions Special Probability Densities Frank Keller 2016
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More on CFG Parsing David R. Mortensen, Alan W. Black 2018
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Local Models Ethem Alpaydın 2014
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Optimization and Neural Nets Pieter Abbeel, Dan Klein 2018
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Decision trees David Sontag 2016
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Optimization: Part II Jorge Nocedal 2018
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Why is NLP Hard? Dragomir Radev 2018
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Writng Well in NLP and ML Chris Dyer 2015
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DAG networks François Fleuret 2019
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Statistical Computing for Scientists and Engineers Nicholas Zabaras 2017
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Unsupervised HMM POS Tagger Comparison Julia Hockenmaier 2013
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Preprocessing Dragomir Radev 2017
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Parsing, and Context-Free Grammars Michael Collins, 2018
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Pattern Recognition and Machine Learning Chapter 1: Introduction Christopher M. Bishop 2016
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Training neural networks - model selection Hugo Larochelle 2017
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Text Corpora Nathan Schneider 2018
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Association Analysis John Paisley 2017
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Data Intensive Linguistics Lecture 16 Philipp Koehn 2008
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Constructions on Finite Automata Shay Cohen 2017
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Factor Analysis and State Space Models Eric Xing 2014
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Bias and Fairness Furong Huang 2018
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NLP Evaluation: Bootstrapping & sig tests Brendan O’Connor 2015
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Basic Concepts K-nearest neighbors Ansaf Salleb-Aouissi 2014
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PREPROCESSING Trevor Cohn 2018
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Language and Complexity Dragomir Radev 2018
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What is Probability Theory? Probability and Cognition Combinatorial Methods 2 Frank Keller 2016
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Introduction Lu Wang 2017
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Machine Learning George Konidaris 2016
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Computational Linguistics Marine Carpuat 2017
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Review of Statistics and Probability Ricardo Gutierrez-Osuna 2013
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Overview Matt Gormley 2016
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Computational Morphology: Introduction Yulia Zinova 2016
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Machine learning II Percy Liang 2017
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Sample Spaces and Events Probability of an Event Frank Keller 2016
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Introduction and Course Overview Sergey Levine 2018
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Decision Trees Ethem Alpaydın 2014
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Learning to Translate (native speakers) Jason Eisner 2012
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Introduction and Class Logistics Dragomir Radev 2017
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Machine Learning Theory Tom M. Mitchell 2015
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Spectral Algorithms for Graphical Models Eric Xing 2014
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Minimum Bayes Risk Chris Dyer 2015
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Introduction Greg Durrett 2018
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Robbins-Monro for Sequential MLE, Curse of Dimensionality, Conditional and Marginal Gaussian Distributions Nicholas Zabaras 2017
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Phonology [These slides are missing most examples and discussion from class ] Jason Eisner 2012
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Parts of Speech Dragomir Radev 2017
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Introduction to Probability and Statistics Chris Callison-Burch 1058
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Perceptron Marine Carpuat 2018
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Natural language processing - preprocessing Hugo Larochelle 2017
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Building Finite-State Machines Jason Eisner 2012
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Coreference Resolution Richard Socher 2018
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Reverse Sampling, Transformation Methods, Composition Methods, Accept-Reject Methods, Stratified/Systematic Sampling Nicholas Zabaras 2017
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Understanding Reparameterisation Gradients Philip Schulz, Wilker Aziz 2018
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Python Basics Dragomir Radev 2018
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VIP Refresher: Linear Algebra and Calculus Afshine Amidi and Shervine Amidi 2018
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(Sub)gradient Descent Furong Huang 2018
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Annotation Nathan Schneider 2018
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Machine Learning David Sontag 2016
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Morphology Parsing Shay Cohen 2017
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Word Distributions Dragomir Radev 2018
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What is Linguistics? Nathan Schneider 2018
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Looking at activations François Fleuret 2019
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Logistic Regression John Paisley 2017
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Introduction to the Course Sameer Singh 2017
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Pushdown automata Shay Cohen 2017
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Lecture 14: Formal Grammars Kai-Wei Chang 2016
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History of NLP Dragomir Radev 2018
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Sample Spaces and Events Probability of an Event Frank Keller 2016
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Introduction Ralph Grishman 2018
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Canonical Correlation Analysis Razvan C. Bunescu 2017
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CS11-747 Neural Networks for NLP Conditioned Generation Graham Neubig Graham Neubig 2017
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limitations of regular languages Shay Cohen 2017
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Approximate Inference: Monte Carlo Methods Eric Xing 2014
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Decision Trees John Paisley 2017
- 0 +
Report
NLP Evaluation Brendan O’Connor 2015
- 0 +
Report
Ensemble Methods Sebastian Raschka 2018
- 0 +
Report
Learning Probabilistic Models Roger Grosse 2018
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Report
Methods used in NLP Dragomir Radev 2016
- 0 +
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Introduction Joan Bruna 2016
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Posterior Regularization: An Integrative Paradigm for Learning Graphical Models Eric Xing 2014
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Stochastic Gradient Descent David McAllester 2017
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Anaphora and coreference Jacob Eisenstein 2014
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Baum-Welch and Entropic Training Ricardo Gutierrez-Osuna 2013
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The Boosting Approach to Machine Learning Maria-Florina Balcan 2015
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Embeddings Jacob Eisenstein 2018
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Conclusion Percy Liang 2017
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Bayesian Cognitive Science Cognitive Frank Keller 2016
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Context-Free Recognition David R. Mortensen, Alan W. Black 2018
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Bayesian Estimation Ethem Alpaydın 2014
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Observe some values of a function Learning in the Limit Jason Eisner 2012
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Markov Chain Monte Carlo and Metropolis-Hasting Algorithm Nicholas Zabaras 2017
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Morphological analysis Jirka Hana, Anna Feldman 2013
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Regular Expressions Dragomir Radev 2018
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How much linguistics is needed for NLP? Chris Dyer 2015
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Back-propagation François Fleuret 2019
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Prior and Hierarchical Modeling Nicholas Zabaras 2017
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Correlated Topic Models Julia Hockenmaier 2013
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NLP Tasks 2 Dragomir Radev 2016
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Lexicalized Probabilistic Context-Free Grammars Michael Collins, 2018
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Pattern Recognition and Machine Learning: Figures (PDF) Christopher M. Bishop 2008
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Restricted Boltzmann Machines Hugo Larochelle 2017
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English Syntax & CFGs Nathan Schneider 2018
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Perceptrons and Logistic Regression Pieter Abbeel, Dan Klein 2018
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Bayesian Linear Regression John Paisley 2017
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Introduction Raymond J. Mooney 2017
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Revision Lecture Shay Cohen 2017
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Gaussian graphical models and Ising models: modeling networks Eric Xing 2014
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Practical Issues: Features, Evaluation, Debugging Furong Huang 2018
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Probability, Naive Bayes Brendan O’Connor 2015
- 0 +
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Ensemble Methods:Boosting, Bagging and Random Forests Ansaf Salleb-Aouissi 2014
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Natural Language Processing Lecture 2 Susan W. Brown 2015
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Probabilities 2 Dragomir Radev 2018
- 0 +
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Distributions Independence Frank Keller 2016
- 0 +
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Finite-state methods for morphology Julia Hockenmaier 2018
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Midterm Review Richard Socher 2016
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Semantic Lexicon Induction Ruihong Huang 2017
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The Probabilistic Approach to Learning from Data Matt Gormley 2016
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Lecture 5: Morphology Kai-Wei Chang 2016
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Machine learning I Percy Liang 2017
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What is Probability Theory? Probability and Cognition Combinatorial Methods Frank Keller 2016
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Viterbi Algorithm Ralph Grishman 2018
- 0 +
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Multivariate Methods Ethem Alpaydın 2014
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Natural Language Processing Lecture 15 Susan W. Brown 2015
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Overfitting and probability Tom M. Mitchell 2015
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Kernel Graphical Models Eric Xing 2014
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Python George Konidaris 2016
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Integer Linear Programming Inference for Conditional Dan Roth 2017
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Importance Sampling Prof. Nicholas Zabaras Nicholas Zabaras 2017
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Bayes Theorem Jason Eisner 2012
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Linguistics Basics Dragomir Radev 2017
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Information Retrieval: Lecture Introduction Christopher Manning 2017
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Linear Models, Naive Bayes Marine Carpuat 2018
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Training neural networks - backpropagation algorithm Hugo Larochelle 2017
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Natural Language Processing: Course Information Dan Klein 2014
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Optimization for Deep Learning Yangfeng Ji 2018
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Gibbs Sampling Nicholas Zabaras 2017
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Graph Partitioning II: Spectral Methods Carlos Castillo 2017
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NLP Resources Dragomir Radev 2018
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Spectral Learning Chris Dyer 2015
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Pattern Recognition and Machine Learning: Solutions to Exercises Christopher M. Bishop 2016
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Review and Practice Problems Furong Huang 2018
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Basic Text Processing Regular Expressions Nathan Schneider 2018
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Introduction to Machine Learning David Sontag 2016
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Pattern Recognition and Machine Learning Christopher M. Bishop 2006
- 0 +
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NLP Tasks 1 Dragomir Radev 2018
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Natural Language Processing Introduction David Bamman 2017
- 0 +
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Looking at parameters François Fleuret 2019
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Introduction to Bayesian Statistics, Exponential Family of Distributions Nicholas Zabaras 2017
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Course Overview Lecture #1 Andrew McCallum 2007
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The Complexity of Human Language Informatics Shay Cohen 2017
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NLTK Dragomir Radev 2018
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Kraft Inequality, Source Coding Theorem, Huffman Coding Frank Keller 2016
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Ambiguity and Context-Free Parsing Ralph Grishman 2018
- 0 +
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Optimization Razvan C. Bunescu 2017
- 0 +
Report
Empirical Methods in Natural Language Processing Lecture 2 Philipp Koehn 2008
- 0 +
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What is programming language semantics? Shay Cohen 2017
- 0 +
Report
Variational Inference: Theory Eric Xing 2014
- 0 +
Report
Bias-Variance Tradeoff John Paisley 2017
- 0 +
Report
Viterbi Algorithm Brendan O’Connor 2015
- 0 +
Report
Data Preprocessing and Machine Learning with Scikit-Learn Sebastian Raschka 2018
- 0 +
Report
Optimization Roger Grosse 2018
- 0 +
Report
Lecture 8: Word Clustering Kai-Wei Chang 2016
- 0 +
Report
Probability and Bayes Theorem Dan Roth 2018
- 0 +
Report
Maximum-Margin Learning of Graphical Models Eric Xing 2014
- 0 +
Report
Invariant Theory David McAllester 2017
- 0 +
Report
Administrative corrections Jacob Eisenstein 2014
- 0 +
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Mixture Models and EM Ricardo Gutierrez-Osuna 2013
- 0 +
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Bayes Rule and Estimating parameters Tom M. Mitchell 2015
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Lecture Notes on Spectral Graph Methods Michael W. Mahoney 2016
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Overview and review Percy Liang 2017
- 0 +
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Expectation and Related Concepts Chebyshevs Theorem Frank Keller 2016
- 0 +
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Words and Morphology David R. Mortensen, Alan W. Black 2018
- 0 +
Report
Combining Multiple Learners Ethem Alpaydın 2014
- 0 +
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Hidden Markov Models and the Forward-Backward Algorithm Jason Eisner 2012
- 0 +
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Why is NLP Hard? Dragomir Radev 2017
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Report
Introduction to Monte Carlo Methods, Sampling from Discrete and Continuum Distributions Nicholas Zabaras 2017
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Tagsets, Corpora Annotation Jirka Hana, Anna Feldman 2013
- 0 +
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Preprocessing Dragomir Radev 2018
- 0 +
Report
Natural Language Parsing with Context-Free Grammars Chris Dyer 2015
- 0 +
Report
Gradient descent François Fleuret 2019
- 0 +
Report
Mixture Models and Expectation-Maximization Nicholas Zabaras 2017
- 0 +
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Discovering Morphological Paradigms from Plain Text Using a Dirichlet Process Mixture Model Julia Hockenmaier 2013
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NLP Tasks 1 Dragomir Radev 2016
- 0 +
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Probabilistic Context-Free Grammars Michael Collins, 2018
- 0 +
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Pattern Recognition and Machine Learning: Figures (JPG) Christopher M. Bishop 2008
- 0 +
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Neural networks Conditional random elds - motivation Hugo Larochelle 2017
- 0 +
Report
Coreference Resolution Nathan Schneider 2018
- 0 +
Report
Naïve Bayes Pieter Abbeel, Dan Klein 2018
- 0 +
Report
Introduction to Natural Language Processing ETHZ 2013
- 0 +
Report
Context-sensitive languages Shay Cohen 2017
- 0 +
Report
Generalized linear models Eric Xing 2014
- 0 +
Report
A Probabilistic View of Machine Learning I Furong Huang 2018
- 0 +
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Natural Language Processing Introduction Brendan O’Connor 2015
- 0 +
Report
Bootstrapping and Face Detection Application Ansaf Salleb-Aouissi 2014
- 0 +
Report
VIP Cheatsheet: Machine Learning Tips Afshine Amidi and Shervine Amidi 2018
- 0 +
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Morphology and Lexicon Dragomir Radev 2018
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Report
Continuous Random Variables and Density 2 Frank Keller 2016
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Introduction Julia Hockenmaier 2018
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Model Selection John Paisley 2017
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Text Classification and Naive Bayes Ruihong Huang 2017
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Optimization for ML Matt Gormley 2016
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Lecture 7: Word Embeddings Kai-Wei Chang 2016
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Introduction Dragomir Radev 2018
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Continuous Random Variables and Density Frank Keller 2016
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Semantic Grammar Ralph Grishman 2018
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Non-parametric Methods Ethem Alpaydın 2014
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Finite state morphology and phonology Susan W. Brown 2015
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Never-Ending Language Learning Maria-Florina Balcan 2015
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Infinite Feature Models: The Indian Buffet Process Eric Xing 2014
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Machine Learning George Konidaris 2016
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Deep Learning for Natural Language Processing: Introductioin Richard Socher 2016
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Sequential Monte Carlo (Continued) and Conditional Linear Gaussian Models Nicholas Zabaras 2017
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Natural Language Processing Jason Eisner 2012
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Report
Bayes Theorem Dragomir Radev 2017
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Auto encoders Joan Bruna 2016
- 0 +
Report
Introduction to Computational Linguistics Marine Carpuat 2018
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Training neural networks - optimization Hugo Larochelle 2017
- 0 +
Report
Natural Language Processing: Tasks Dan Klein 2014
- 0 +
Report
Fourier Analysis Ricardo Gutierrez-Osuna 2013
- 0 +
Report
Overfitting and probability Tom M. Mitchell 2015
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Report
Lecture Slides for Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares Stephen Boyd, Lieven Vandenberghe 2018
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Differential Equations and Linear Algebra Mathworks 2017
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Special Probability Distributions Special Probability Densities Frank Keller 2016
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A First Look at Structures Dan Roth 2017
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