Topic 5: Classification 552


Title (link) Lecturer Date Votes Error
Learning: Support Vector Machines Patrick Winston 2014
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First Quora Dataset Release: Question Pairs Shankar Iyer, Nikhil Dandekar, Kornél Csernai 2017
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Multi-clustering Datasets Sajib Dasgupta and Vincent Ng 2010
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Title (link) Lecturer Date Votes Error
Gaussian Processes for Classification Problems, Course Summary Nicholas Zabaras 2017
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The Perceptron Furong Huang 2018
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Classification with Scattering Joan Bruna 2016
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Semi-Supervised Learning Maria-Florina Balcan 2015
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Discriminative Training part 2 Chris Callison-Burch 1155
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K-Means Clustering (Unsupervised Learning) Furong Huang 2018
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Natural Language Processing: Classification I Dan Klein 2014
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Log Linear Models Yejin Choi 2018
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VIP Cheatsheet: Unsupervised Learning Afshine Amidi and Shervine Amidi 2018
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Nearest Neighbor Methods Sebastian Raschka 2018
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SVMs Christopher Manning 2017
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Practical Considerations of Classification Dragomir Radev 2016
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Decision Trees Tom M. Mitchell 2015
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Kernel Methods and Introduction to Gaussian Processes Nicholas Zabaras 2017
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Natural Language Processing Classification III Dan Klein 2014
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Recap Clustering: Introduction Hinrich Sch¨utze 2014
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k-Nearest Neighbors Matt Gormley 2016
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Probability Distributions on Structured Objects Chris Dyer 2015
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Generative and Discriminative Models 1 Dragomir Radev 2017
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Support Vector Machines Ricardo Gutierrez-Osuna 2013
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Loss-augmented Structured Prediction Marine Carpuat 2018
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Binary classification Greg Durrett 2018
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Support vector machines (SVMs) David Sontag 2016
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The Perceptron Furong Huang 2018
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Linear Models for Classication: Features & Weights Nathan Schneider 2018
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Semi-Supervised Learning 1 Jacob Eisenstein 2014
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Logistic Regression Marine Carpuat 2018
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Discriminative Training Chris Callison-Burch 1155
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K-Means and GMMs Matt Gormley 2016
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Text Classification Yoav Artzi 2018
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Evaluation of Text Classification Dragomir Radev 2016
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Log-linear models and CRFs Brendan O’Connor 2015
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Supervised Learning Ethem Alpaydın 2014
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Decision Trees Sebastian Raschka 2018
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SVMs Christopher Manning 2017
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Practical Considerations of Classification Dragomir Radev 2018
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Decision Trees Dan Roth 2018
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Bayesian Linear Regression (continued) Nicholas Zabaras 2017
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Tree classifiers Ansaf Salleb-Aouissi 2014
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Support vector machines (SVMs) Lecture 3 David Sontag 2016
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Semi-Supervised Learning Jacob Eisenstein 2017
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Feature Selection Dragomir Radev 2017
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Math Review and Decision Trees Maria-Florina Balcan 2015
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Structured Prediction for Language and Other Discrete Data Chris Dyer 2015
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Linear separability and feature design François Fleuret 2019
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SVMs and Kernel Methods Ricardo Gutierrez-Osuna 2013
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Structured Prediction Basics Dan Roth 2017
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Multiclass classification Greg Durrett 2018
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Naive Bayes Classifier Ansaf Salleb-Aouissi 2014
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Support Vector Machines: primal, dual forms and soft-margin Ansaf Salleb-Aouissi 2014
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The Perceptron Razvan C. Bunescu 2017
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Classification Brendan O’Connor 2015
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Supervised Linear Regression Xavier Bresso 2017
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Discriminative Sequence Nathan Schneider 2018
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Clustering(K-Means) Matt Gormley 2016
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Mathematics for Machine Learning: Classification with Support Vector Machines Marc Peter Deisenroth, A Aldo Faisal, Cheng Soon Ong 2018
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Text Classification Contd + Document Representations Sameer Singh 2017
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Nearest Neighbors Ricardo Gutierrez-Osuna 2013
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Support Vector Machines Hinrich Sch¨utze 2014
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Maximum Likelihood and Gaussian Models Ansaf Salleb-Aouissi 2014
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Seperable Scattering Operators Joan Bruna 2016
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Bayesian Regression Nicholas Zabaras 2017
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Structured Perceptron, Viterbi Marine Carpuat 2018
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Supervised Learning Methods k-nearest-neighbors(k-NN) Decisiontrees(Chapter18.3) Neuralnetworks(ANN) Supportvectormachines(SVM) Chuck Dyer 2018
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Feature Selection Dragomir Radev 2018
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Logistic Regression 1 Dragomir Radev 2017
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Linear regression Tom M. Mitchell 2015
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Lecture 15: Structured Prediction Kevin Gimpel 2017
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Maximum Entropy Models and Feature Engineering Ralph Grishman 2018
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Meta-Learning Sergey Levine 2018
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Probabilistic view of a linear classifier François Fleuret 2019
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Linear Regression, Ridge regression, and Lasso Ansaf Salleb-Aouissi 2014
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Logistic Regression 1 Dragomir Radev 2018
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Linear Models for Classication: Discriminative Learning (Perceptron, SVMs, MaxEnt) Nathan Schneider 2018
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Multi-layer perceptrons Ethem Alpaydın 2014
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Classication key concepts Jacob Eisenstein 2014
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Overview Statistical NLP Tom Kwiatkowski 2016
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Flat Clustering Hinrich Sch¨utze 2014
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Text Classification 1 Sameer Singh 2017
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Evaluation of Text Classification Dragomir Radev 2018
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Log-linear models Jonathan Berant 2017
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Unsupervised Learning Greg Durrett 2018
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Feature Expansions John Paisley 2017
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Encoder-Decoder Neural Networks Nal Kalchbrenner 2017
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Statistical Clustering Ricardo Gutierrez-Osuna 2013
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Structure and Support Vector Machines Chris Dyer 2015
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Mixture Modeling Roger Grosse 2018
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Random Forests Joan Bruna 2016
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Linear classification: Logistic Regression Ansaf Salleb-Aouissi 2014
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Soft Clustering vs Hard Clustering John Paisley 2017
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The perceptron François Fleuret 2019
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Supervised Learning Methods k-nearest-neighbors (k-NN) Decision trees Support vector machines (SVM) Neural networks Chuck Dyer 2018
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Linear classifiers part I Svetlana Lazebnik 2018
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Generative and Discriminative Models 1 Dragomir Radev 2018
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Linear regression Tom M. Mitchell 2015
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Feature Selection Sebastian Raschka 2018
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Text Classification 1 Christopher Manning 2017
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Global linear models Jonathan Berant 2017
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Tree Classifiers (Decision Trees) Ansaf Salleb-Aouissi 2014
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Logistic Regression Ansaf Salleb-Aouissi 2014
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INFORMATION EXTRACTION Trevor Cohn 2018
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Multilayer Perceptrons Roger Grosse 2018
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Feature-based Discriminative Models More Sequence Models Yoav Goldberg 2018
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Statistical NLP Tom Kwiatkowski 2016
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Hilbert Space Embeddings of Distributions Eric Xing 2014
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Text Categorization Raymond J. Mooney 2017
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Support vector machines (SVMs) David Sontag 2016
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Lecture 1: Introduction Julia Hockenmaier 2013
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Transformation Groups Joan Bruna 2016
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Unsupervised learning (part1) David Sontag 2016
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Examples of Text Classification Dragomir Radev 2016
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Learning Generative Models Chris Dyer 2015
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Logistic Regression, Nonlinear Features, Regularization Matt Gormley 2016
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Clustering Ethem Alpaydın 2014
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Multi-Layer Perceptrons François Fleuret 2019
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Geometry and Nearest Neighbors Furong Huang 2018
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Perceptron Dragomir Radev 2017
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Linear classifiers part II Svetlana Lazebnik 2018
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Bayes Nets Dan Klein and Pieter Abbeel 2014
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Classification, kernels and metrics Joan Bruna 2016
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Feature Selection Sebastian Raschka 2018
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Text Classification 1 Christopher Manning 2017
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Evaluation Ralph Grishman 2018
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Linear models review Svetlana Lazebnik 2018
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Linear Classification: Perceptron Ansaf Salleb-Aouissi 2014
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Multiclass Logistic Regression David McAllester 2017
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Word/Sentence Embeddings, Text Classification Mohit Bansal 2017
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Kernels Methods in Machine Learning Maria-Florina Balcan 2015
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Text Classification and Nave Bayes The Task of Text Classification Dan Jurafsky 2017
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Text Clustering Raymond J. Mooney 2017
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Lecture 2: Text Classification Kevin Gimpel 2016
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Support vector machines (SVMs) David Sontag 2016
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Local invariants and convolution Joan Bruna 2016
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Generative Models David Duvenaud 2018
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Machine Learning in Practice + k-Nearest Neighbors Matt Gormley 2016
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Lecture 16: Structured Prediction in NLP, Syntactic & Semantic Formalisms Kevin Gimpel 2017
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Classification & Information Theory Lecture #8 Andrew McCallum 2007
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Regression for classification Jinho D. Choi 2017
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Hierarchical & Spectral clustering David Sontag 2016
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Discriminative Models: MaxEnt,Perceptron Ruihong Huang 2017
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Multiclass Classification and Reductions Furong Huang 2018
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Multi-class classification Svetlana Lazebnik 2018
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Linear Discrimination Ethem Alpaydın 2014
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Multi-Class Classification Kai-Wei Chang 2017
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Sequential Feature Selection Ricardo Gutierrez-Osuna 2013
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Generative and Discriminative Learning Vivek Srikumar 2018
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Vector Space Classification Hinrich Sch¨utze 2014
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Evaluation Methods Sebastian Raschka 2018
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Linear Regression, Least Squares and Gradient Descent Ansaf Salleb-Aouissi 2014
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Least Squares Continued John Paisley 2017
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10-601 Machine Learning Maria-Florina Balcan Maria-Florina Balcan 2015
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VIP Cheatsheet: Supervised Learning Afshine Amidi and Shervine Amidi 2018
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Empirical Methods in Natural Language Processing Lecture 12 Philipp Koehn 2008
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Natural Language Processing (CSEP 517): Text Classication Noah Smith 2017
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Support Vector Machines:Introduction Ansaf Salleb-Aouissi 2014
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Binary Classification with Linear Models Furong Huang 2018
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Features and hypothesis tests David Bamman 2017
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Generative Models II Phillip Isola 2018
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Hierarchical Clustering Hinrich Sch¨utze 2014
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Lecture 8: Inference in Structured Prediction Kevin Gimpel 2016
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Logistic Regression Brendan O’Connor 2015
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Multilayer Perceptrons Ricardo Gutierrez-Osuna 2013
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Quadratic Classifiers Ricardo Gutierrez-Osuna 2013
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Naive Bayes: Text Classification Ansaf Salleb-Aouissi 2014
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Binary Classification Kai-Wei Chang 2017
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Randomized Feature Selection Ricardo Gutierrez-Osuna 2013
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Logistic regression, Loss Functions, Neural Networks Marine Carpuat 2018
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Classification and kNN 1 Dragomir Radev 2017
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Text Classification & Naive Bayes Hinrich Sch¨utze 2014
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Support Vector Machines: Kernels Ansaf Salleb-Aouissi 2014
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Evaluation Methods Sebastian Raschka 2018
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Learning a Classier Roger Grosse 2018
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Linear Regression John Paisley 2017
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Perceptron Dan Roth 2018
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CS11-747 Neural Networks for NLP A Simple (?) Exercise:Predicting the Next Word Graham Neubig 2017
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Maximum Margin Classifiers John Paisley 2017
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Indian Buffet Process Julia Hockenmaier 2013
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Guiding Semi-Supervision with Constraint-Driven Learning Dan Roth 2017
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SVMs Dan Roth 2017
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CS11-747 Neural Networks for NLP Structured Prediction with Local Dependencies Graham Neubig 2017
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Classifcaaon David R. Mortensen, Alan W. Black 2018
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Logistic Regression & Neural Networks Marine Carpuat 2017
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Introduction to Support Vector Machines and Kernels Dragomir Radev 2018
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Linear Discriminant Functions Ricardo Gutierrez-Osuna 2013
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Unsupervised learning Ansaf Salleb-Aouissi 2014
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Gaussian Naive Bayes Matt Gormley 2016
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Clustering 1 Dragomir Radev 2018
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Classification David Bamman 2017
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Manifold Learning Ricardo Gutierrez-Osuna 2013
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Text Classification & Linear Models Marine Carpuat 2017
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TEXT CLASSIFICATION Trevor Cohn 2018
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Support vector machines (SVMs) Matt Gormley 2016
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Evaluation Methods Sebastian Raschka 2018
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Linear Regression Roger Grosse 2018
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Minimum L2 Regression John Paisley 2017
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CRF and Structured Perceptron Brendan O’Connor 2015
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Clustering David Sontag 2016
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Linear Classification John Paisley 2017
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Lecture 22: Representation Learning Kai-Wei Chang 2016
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Semi-supervised Learning for NLP Richard Socher 2018
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Kernel Density Estimation Ricardo Gutierrez-Osuna 2013
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CS11-747 Neural Networks for NLP Structured Prediction Basics Graham Neubig 2017
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Multiclass Classification Dan Roth 2017
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SVM Ansaf Salleb-Aouissi 2014
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Perceptron Dragomir Radev 2018
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Logistic regression, Regularization Yangfeng Ji 2018
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Unsupervised Learning George Konidaris 2016
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Logistic regression and Generative/Discriminative classifiers Tom M. Mitchell 2015
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Kernels and Clustering Dan Klein and Pieter Abbeel 2014
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Classification 2 David Bamman 2017
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Independent Components Analysis Ricardo Gutierrez-Osuna 2013
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Linear Models Ansaf Salleb-Aouissi 2014
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Support Vector Machines (SVMs) Maria-Florina Balcan 2015
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Evaluation Methods Sebastian Raschka 2018
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Naive Bayes Tom M. Mitchell 2015
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Machine Learning: Logistic Regression Razvan C. Bunescu 2017
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Naïve Bayes,Perceptron Jacob Eisenstein 2014
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K-Means, GMMs and EM Ansaf Salleb-Aouissi 2014
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Classification John Paisley 2017
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Latent Graphical Models Joan Bruna 2016
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Classification and kNN 2 Dragomir Radev 2017
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Radial Basis Functions Ricardo Gutierrez-Osuna 2013
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Lecture 13: Structured Prediction Kai-Wei Chang 2016
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Classification and kNN 1 Dragomir Radev 2018
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Multiclass Classification Dan Roth 2017
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Kernel Methods Ethem Alpaydın 2014
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Perceptrons Dan Klein and Pieter Abbeel 2014
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Supervised Classification Xavier Bresso 2017
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Unsupervised Learning John Paisley 2017
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Logistic regression and Generative/Discriminative classifiers Tom M. Mitchell 2015
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Constraint Satisfaction Problems II Dan Klein and Pieter Abbeel 2014
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Classification 3 David Bamman 2017
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Log-Linear Models Michael Collins, 2018
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Statistical learning, kNN, linear classifiers Svetlana Lazebnik 2018
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Support Vector Machines Dan Roth 2018
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Evaluation Methods Sebastian Raschka 2018
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Naive Bayes Tom M. Mitchell 2015
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Introduction to Bayesian Linear Regression, Model Comparison and Selection Nicholas Zabaras 2017
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Natural Language Processing Classification II Dan Klein 2014
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Clustering. Unsupervised Learning Maria-Florina Balcan 2015
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k-Nearest Neighbors Razvan C. Bunescu 2017
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Structured Prediction Models Kai-Wei Chang 2017
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Examples of Text Classification Dragomir Radev 2018
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Classification and kNN 2 Dragomir Radev 2018
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MLPs, RBFs and Statistical PR Ricardo Gutierrez-Osuna 2013
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Global and Local Views Dan Roth 2017
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Title (link) Author Date Votes Error
The Automatic Statistician Christian Steinruecken, Emma Smith, David Janz, James Lloyd, Zoubin Ghahramani 2018
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Introduction to Semi-Supervised Learning Xiaojin Zhu and Andrew B. Goldberg 2008
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Machine Learning, Neural and Statistical Classification D. Michie, D.J. Spiegelhalter, C.C. Taylor 1994
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Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly Yongqin Xian, Christoph H. Lampert, Bernt Schiele, Zeynep Akata 1037
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Graph-Based Semi-Supervised Learning Amarnag Subramanya, Partha Pratim Talukdar 2014
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Text Classification Algorithms: A Survey Kamran Kowsari 2019
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Vector Semantics Daniel Jurafsky, James H. Martin 2017
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Semantics with Dense Vectors Daniel Jurafsky, James H. Martin 2017
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Understanding Neural Networks via Feature Visualization: A survey "Anh Nguyen, Jason Yosinski, and Jeff Clune" 2019
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Naive Bayes and Text Classification Sebastian Raschka 2014
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Learning with Support Vector Machines Colin Campbell and Yiming Ying 2011
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Semi-Supervised Learning and Domain Adaptation in Natural Language Processing Anders Sogaard 2013
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Computing with Word Senses Daniel Jurafsky, James H. Martin 2017
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Vector Semantics Daniel Jurafsky 2017
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A Survey on Multi-output Learning "Donna Xu, Yaxin Shi, Ivor W. Tsang, Yew-Soon Ong, Chen Gong, and Xiaobo Shen" 2019
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Inductive Semi-supervised Learning with Applicability to NLP Anoop Sarkar and Gholamreza Haffari 2015
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A Survey of Text Classification Algorithms Charu C. Aggarwal, ChengXiang Zhai 2012
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Vector Semantics (Dense Vectors) Daniel Jurafsky 2017
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Logistic Regression Daniel Jurafsky, James H. Martin 2017
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Text Classification Algorithms: A Survey Kamran Kowsari 2019
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Title (link) Author Date Votes Error
Support Vector Machine (SVM) Explorer Xing Han Lu, Matthew Chan 2018
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SVMpy Andrew Tulloch 2013
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Feature extraction and reverse image search ml4a 2018
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Text Classification Models with Tensorflow dongjun-Lee 2018
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Deep Text Classifier Matvey Ezhov 2017
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Non-Metric Space Library (NMSLIB): An efficient similarity search library Leonid Boytsov 2018
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Support Vector Machine in Javascript Andrej Karpathy 2017
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LIBSVM -- A Library for Support Vector Machines Chih-Jen Lin 2016
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Approximate Nearest Neighbors in C++/Python Erik Bernhardsson 2018
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Bayesian Unsupervised Topic Segmentation Jacob Eisenstein and Regina Barzilay 2018
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Classification, and scaling up our networks ml4a 2018
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Planetoid Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov 2016
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Implementation of the Brown hierarchical word clustering algorithm Percy Liang 2007
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dtreeviz : Decision Tree Visualization Terence Parr 2018
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Hierarchical word clustering, following "Brown clustering" Michael Heilman 2013
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Text Classification Author Unknown 2017
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Introduction to classification ml4a 2018
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Introduction to Implementing scikit-learn Classifiers Mathew Mayo 2016
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ULMFiT_fastai_Text_Classification prateekjoshi565 2018
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DensiTree: making sense of sets of trees Remco Bouckaert 2011
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fasttext Sale Stock 2017
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Title (link) Author Date Votes Error
Complete tutorial on Text Classification using Conditional Random Fields Model (in Python) Pranav Dar 2018
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Chapter 1.3:Code for linear regression with Gradient descent (from scratch and Tensorflow & Scikit Learn ). Madhu Sanjeevi 2017
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Text Classification Vered Shwartz 2016
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Logistic Regression: a quick introduction Irene Li 2015
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Beyond Accuracy: Precision and Recall William Koehrsen 2018
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Clustering text documents using scikit-learn kmeans in Python Various Authors 2018
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Logistic and Softmax Regression Xianxu Hou 2015
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Deep Generative Models: A Unified Statistical View Petuum, Inc. 2018
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Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning and Big Data Stefan Kojouharov 2017
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Text Classification Author Unknown 2018
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Chapter 2.1: Code for logistic regression with Gradient descent (from scratch and also using Scikit Learn ). Madhu Sanjeevi 2017
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Multi-Class Text Classification Model Comparison and Selection Susan Li 2018
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Text Mining: how to cluster texts (e.g. news articles) with artificial intelligence? daniel451 2015
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The Perceptron Matt Mazur 2014
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Simple Softmax Regression in Python?—?Tutorial Arthur Juliani 2016
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Generalization and Equilbrium in Generative Adversarial Nets (GANs) Sanjeev Arora 2017
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Linearly Separable? No? For me it is! A Brief introduction to Kernal Methods Giuseppe Bonaccorso 2017
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Multi-Class Text Classification with Doc2Vec & Logistic Regression Susan Li 2018
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Short Text Categorization using Deep Neural Networks and Word-Embedding Models Kwan-Yuet (Stephen) Ho 2016
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Unsupervised Learning with Python Vihar Kurama 2018
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Comparing Python Clustering Algorithms Leland McInnes, John Healy, Steve Astels 2016
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Understanding AI Toolkits Edd Wilder-James 2017
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Difference Between Softmax Function and Sigmoid Function Saimadhu Polamuri 2017
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Expectation Maximization Joydeep Bhattacharjee 2018
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GANs are Broken in More than One Way: The Numerics of GANs Ferenc Huszár 2017
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Supervised Learning with Python Vihar Kurama 2018
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Comprehensive Support Vector Machines Guide - Using Illusion to Solve Reality! Pranov Mishra 2018
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Linear Regression Simplified - Ordinary Least Square vs Gradient Descent Prabhu 2018
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A Tutorial on Spectral Clustering Chris Ding 2004
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Recipe: Text classification using NLTK and scikit-learn bogdani 2016
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25 Questions to test a Data Scientist on Support Vector Machines Ankit Gupta 2017
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Report on Text Classification using CNN, RNN & HAN Akshat Maheshwari 2018
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Text Classification with State of the Art NLP Library — Flair Tadej Magajna 2018
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An Overview of Proxy-label Approaches for Semi-supervised Learning Sebastian Ruder 2018
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Unsupervised Learning Joydeep Bhattacharjee 2018
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Building A Logistic Regression in Python, Step by Step Susan Li 2017
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A Tutorial on Spectral Clustering Part 2: Advance/related Topics Chris Ding 2004
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Introducing state of the art text classification with universal language models Jeremy Howard, Sebastian Ruder 2018
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Logistic Regression Jatin Ahuja 2018
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Perceptrons, SVMs and Kernel Methods Daniel Seita 2015
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Beginning SVM from Scratch in Python Harrison 2018
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Linear Discriminant Analysis Srishti Sawla 2018
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Improving Language Understanding with Unsupervised Learning Alec Radford 2018
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Introduction to k-Nearest-Neighbors Devin Soni 2018
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Chapter 3: Support Vector machine with Math. Madhu Sanjeevi 2017
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Machine Learning Basics?—?Part 3?—?Vector Machines, Unsupervised Learning and Principal Component Analysis Daniel Deutsch 2018
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Multi Label Text Classification with Scikit-Learn Susan Li 2018
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Linear Regression?—?Detailed View Saishruthi Swaminathan 2018
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Text Classification using Algorithms gk_ 2017
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Introduction to Pseudo-Labelling : A Semi-Supervised learning technique Shubham Jain 2017
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A different kind of (deep) learning: part 1 Gidi Shperber 2018
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Demystifying Maths of SVM Krishna Kumar Mahto 2019
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Chapter 5 : K-nearest neighbors algorithm with code from scratch. Madhu Sanjeevi 2017
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