Topic 5: Classification 364


Title (link) Author Date Votes Error Reviews
Semi-supervised Learning Tutorial John Blitzer and Xiaojin Zhu 2008
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Machine Learning Classics : The Perceptron The Data Science Lab 2014
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Text Classifier Algorithms in Machine Learning Roman Trusov 2017
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Max-Margin Methods for NLP: Estimation, Structure and Applications Dan Klein and Ben Taskar 2005
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ACL 2008: Semi-supervised Learning Tutorial John Blitzer and XiaojinZhu 2008
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An Idiot's guide to Support vector machines (SVMs) Robert Berwick 2003
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Machine Learning Methods in Natural Language Processing Michael Collins 2003
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A Tutorial on Graph-based Semi-Supervised Learning Algorithms for NLP Amarnag Subramanya and Partha Pratim Talukdar 2012
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Logistic Regression TRIANGLEINEQUALITY 2013
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Example of logistic regression in Python using scikit-learn Kevin Markham 2014
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A Real Example: Logistic Regression Theano Development Team 2017
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Classifying MNIST digits using Logistic Regression LISA lab 2014
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Logistic Regression - Geometric Intuition Florian Hartl 2015
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Support Vector Machine (and Statistical Learning Theory) Tutorial Jason Weston 2006
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Clustering With K-Means in Python datasciencelab 2013
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Selection of K in K-means Clustering, Reloaded datasciencelab 2014
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K- means clustering with scipy glowingpython 2012
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K-Means Clustering with TensorFlow sachin joglekar 2015
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Visualizing K-Means Clustering Karanveer Mohan 2014
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K-Nearest Neighbour Classifier glowingpython 2012
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k-nearest neighbor search glowingpython 2012
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Enter the Perceptron Triangle Inequality 2014
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The Perceptron The Glowing Python 2011
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Introduction, Implementing Classifiers Mathew Mayo, KDnuggets 2016
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Unsupervised Machine Learning: Flat Clustering Harrison Kinsley 2015
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Linear SVC Machine Learning SVM Example with Python Harrison Kinsley 2015
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A basic soft-margin kernel SVM implementation in Python Andrew Tulloch 2013
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What does support vector machine (SVM) mean in layman's terms? Akihiro Matsukawa 2014
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How does deep learning work and how is it different from normal neural netowrks applied with SVM? Patrick Hall 2016
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Tree Kernels: Quantifying Similarity Among Tree-Structured Data Dino Causevic 2016
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Implementing Your Own k-Nearest Neighbour Algorithm Using Python Natasha Latysheva 2016
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The k-Means Algorithm Hannes Schulz, Andreas Mueller and Nenard Birešev 2013
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News Categorization using Multinomial Naive Bayes Andres Soto Villaverde 2016
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Document Clustering Example in SciKit-Learn Chris McCormick 2015
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SVM Tutorial - Part I Chris McCormick 2013
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Logistic Regression Goku Mohandas 2016
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Rohan #1: Logistic regression case study?—?diagnosing cancer Rohan Kapur 2016
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Rohan #6: The beautiful math behind logistic regression Rohan Kapur 2016
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MACHINE LEARNING WITH MISSING LABELS PART 3: EXPERIMENTS Charles H. Martin 2013
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MACHINE LEARNING WITH MISSING LABELS PART 2: THE UNIVERSVM Charles H. Martin 2014
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MACHINE LEARNING WITH MISSING LABELS: TRANSDUCTIVE SVMS Charles H. Martin 2014
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Support Vector Machines for Machine Learning Jason Brownlee 2016
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Naive Bayes Tutorial for Machine Learning Jason Brownlee 2016
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Naive Bayes for Machine Learning Jason Brownlee 2016
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How To Implement Naive Bayes From Scratch in Python Jason Brownlee 2014
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Tutorial To Implement k-Nearest Neighbors in Python From Scratch Jason Brownlee 2014
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cs231n notes: Linear Classification Andrej Karpathy 2016
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cs231n notes: Classification Andrej Karpathy 2016
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Lecture 1 Introduction to Kernel Methods Bharath K. Sriperumbudur 2017
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TensorFlow Linear Model Tutorial - TensorFlow Tutorials TensorFlow Team 2017
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Improving Linear Models Using Explicit Kernel Methods - TensorFlow Tutorials TensorFlow Team 2017
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Logistic Regression from Scratch in Python Nick Becker 2017
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Applied Machine Learning:?Algorithms, Practice and Theory??Predicting & Naïve Bayes Koby Crammer 2011
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Generative Models - The Math of Intellligence #8 Siraj Raval 2017
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Generative Models I Ian Goodfellow 2017
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Faster AI:Lesson 1 - TL;DR version of Fast.ai Part 1 Kshitiz Rimal 2017
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Faster AI:Lesson 0 - TL;DR version of Fast.ai Part 1 Kshitiz Rimal 2017
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Faster AI: Lesson 2 - TL;DR version of Fast.ai Part 1 Kshitiz Rimal 2017
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Reuters-21578 text classification with Gensim and Keras Giuseppe Bonaccorso 2017
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Adversarial Examples and Adversarial Training Ian Goodfellow 2017
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Support Vector Machines (SVM) Tutorial Abhishek Ghose 2017
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K-Means & Other Clustering Algorithms: A Quick Intro with Python Nikos Koufos 2017
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6 Easy Steps to Learn Naive Bayes Algorithm (with code in Python) Emmanuelle Rieuf 2017
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Building a Logistic Regression in Python, Step by Step Susan Li 2017
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A Simple Introduction to Support Vector Machines Martin Law 2011
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Unsupervised Learning Alta de Waal 2017
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Executive Summary: Bayes Nets Andrew W. Moore 2001
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Clustering with Gaussian Mixtures Andrew W. Moore 2001
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Predicting Real-valued outputs: an introduction to Regression Andrew W. Moore 2001
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Kmeans and Hierarchical Clustering Andrew W. Moore 2001
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Instance based Learning Andrew W. Moore 2001
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Naive Bayes Andrew W. Moore 2001
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Regression and Classification with Neural Networks Andrew W. Moore 2001
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SVMs Andrew W. Moore 2001
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Adversarial Examples Ian Goodfellow 2015
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How to Train a GAN Soumith Chintala, Emily Denton, Martin Arjovsky, Michael Mathieu 2017
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Sampling for unsupervised language learning Wilker Aziz 2015
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Supervised Learning Kyunghyun Chuo 2018
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Introduction to Classification: Likelihoods, Margins, Features, and Kernels Dan Klein 2007
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Title (link) Lecturer Date Votes Error Reviews
Classification and kNN 1 Dragomir Radev 2017
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Classification and kNN 2 Dragomir Radev 2017
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Generative and Discriminative Models 1 Dragomir Radev 2017
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Perceptron Dragomir Radev 2017
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Logistic Regression 1 Dragomir Radev 2017
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Examples of Text Classification Dragomir Radev 2016
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Feature Selection Dragomir Radev 2017
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Evaluation of Text Classification Dragomir Radev 2016
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Practical Considerations of Classification Dragomir Radev 2016
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Generative and Discriminative Models 1 Dragomir Radev 2018
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Clustering 1 Dragomir Radev 2018
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Examples of Text Classification Dragomir Radev 2018
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Introduction to Support Vector Machines and Kernels Dragomir Radev 2018
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Practical Considerations of Classification Dragomir Radev 2018
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Classification and kNN 1 Dragomir Radev 2018
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Logistic Regression 1 Dragomir Radev 2018
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Perceptron Dragomir Radev 2018
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Classification and kNN 2 Dragomir Radev 2018
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Feature Selection Dragomir Radev 2018
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Evaluation of Text Classification Dragomir Radev 2018
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Logistic Regression Ansaf Salleb-Aouissi 2014
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Linear Models Ansaf Salleb-Aouissi 2014
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SVM Ansaf Salleb-Aouissi 2014
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Tree classifiers Ansaf Salleb-Aouissi 2014
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Unsupervised learning Ansaf Salleb-Aouissi 2014
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Unsupervised Learning George Konidaris 2016
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Kernels and Clustering Dan Klein and Pieter Abbeel 2014
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Bayes Nets Dan Klein and Pieter Abbeel 2014
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Constraint Satisfaction Problems II Dan Klein and Pieter Abbeel 2014
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Perceptrons Dan Klein and Pieter Abbeel 2014
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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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Supervised Learning Methods k-nearest-neighbors (k-NN) Decision trees Support vector machines (SVM) Neural networks Chuck Dyer 2018
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Multiclass Logistic Regression David McAllester 2017
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Geometry and Nearest Neighbors Furong Huang 2018
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The Perceptron Furong Huang 2018
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K-Means Clustering (Unsupervised Learning) Furong Huang 2018
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The Perceptron Furong Huang 2018
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Multiclass Classification and Reductions Furong Huang 2018
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Binary Classification with Linear Models Furong Huang 2018
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Unsupervised Learning John Paisley 2017
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Maximum Margin Classifiers John Paisley 2017
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Feature Expansions John Paisley 2017
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Linear Classification John Paisley 2017
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Least Squares Continued John Paisley 2017
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Linear Regression John Paisley 2017
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Minimum L2 Regression John Paisley 2017
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Soft Clustering vs Hard Clustering John Paisley 2017
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Classification John Paisley 2017
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k-Nearest Neighbors Razvan C. Bunescu 2017
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The Perceptron Razvan C. Bunescu 2017
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Machine Learning: Logistic Regression Razvan C. Bunescu 2017
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Clustering Ethem Alpaydın 2014
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Supervised Learning Ethem Alpaydın 2014
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Kernel Methods Ethem Alpaydın 2014
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Linear Discrimination Ethem Alpaydın 2014
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Multi-layer perceptrons Ethem Alpaydın 2014
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Introduction to Bayesian Linear Regression, Model Comparison and Selection Nicholas Zabaras 2017
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Kernel Methods and Introduction to Gaussian Processes Nicholas Zabaras 2017
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Gaussian Processes for Classification Problems, Course Summary Nicholas Zabaras 2017
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Bayesian Linear Regression (continued) Nicholas Zabaras 2017
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Bayesian Regression Nicholas Zabaras 2017
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Unsupervised learning (part1) David Sontag 2016
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Hierarchical & Spectral clustering David Sontag 2016
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Clustering David Sontag 2016
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Support vector machines (SVMs) Lecture 3 David Sontag 2016
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Support vector machines (SVMs) David Sontag 2016
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Support vector machines (SVMs) David Sontag 2016
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Support vector machines (SVMs) David Sontag 2016
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Support Vector Machines:Introduction Ansaf Salleb-Aouissi 2014
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Support Vector Machines: Kernels Ansaf Salleb-Aouissi 2014
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Linear classification: Logistic Regression Ansaf Salleb-Aouissi 2014
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K-Means, GMMs and EM 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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Maximum Likelihood and Gaussian Models Ansaf Salleb-Aouissi 2014
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Naive Bayes Classifier Ansaf Salleb-Aouissi 2014
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Linear Regression, Ridge regression, and Lasso Ansaf Salleb-Aouissi 2014
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Tree Classifiers (Decision Trees) Ansaf Salleb-Aouissi 2014
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Linear Classification: Perceptron Ansaf Salleb-Aouissi 2014
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Naive Bayes: Text Classification Ansaf Salleb-Aouissi 2014
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Linear Regression, Least Squares and Gradient Descent Ansaf Salleb-Aouissi 2014
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Multilayer Perceptrons Roger Grosse 2018
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Mixture Modeling Roger Grosse 2018
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Learning a Classier Roger Grosse 2018
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Linear Regression Roger Grosse 2018
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Support vector machines (SVMs) Matt Gormley 2016
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Gaussian Naive Bayes Matt Gormley 2016
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Machine Learning in Practice + k-Nearest Neighbors Matt Gormley 2016
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Logistic Regression, Nonlinear Features, Regularization Matt Gormley 2016
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K-Means and GMMs Matt Gormley 2016
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k-Nearest Neighbors Matt Gormley 2016
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Clustering(K-Means) Matt Gormley 2016
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Naive Bayes Tom M. Mitchell 2015
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Support Vector Machines (SVMs) Maria-Florina Balcan 2015
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Semi-Supervised Learning Maria-Florina Balcan 2015
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Math Review and Decision Trees Maria-Florina Balcan 2015
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Naive Bayes Tom M. Mitchell 2015
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Clustering. Unsupervised Learning Maria-Florina Balcan 2015
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Logistic regression and Generative/Discriminative classifiers Tom M. Mitchell 2015
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Linear regression Tom M. Mitchell 2015
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Kernels Methods in Machine Learning Maria-Florina Balcan 2015
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10-601 Machine Learning Maria-Florina Balcan Maria-Florina Balcan 2015
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Decision Trees Tom M. Mitchell 2015
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Linear regression Tom M. Mitchell 2015
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Logistic regression and Generative/Discriminative classifiers Tom M. Mitchell 2015
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Decision Trees Dan Roth 2018
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Perceptron Dan Roth 2018
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Support Vector Machines Dan Roth 2018
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Classification with Scattering Joan Bruna 2016
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Seperable Scattering Operators Joan Bruna 2016
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Latent Graphical Models Joan Bruna 2016
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Transformation Groups Joan Bruna 2016
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Random Forests Joan Bruna 2016
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Local invariants and convolution Joan Bruna 2016
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Classification, kernels and metrics Joan Bruna 2016
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Text Classification 1 Christopher Manning 2017
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Text Classification 1 Christopher Manning 2017
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SVMs Christopher Manning 2017
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SVMs Christopher Manning 2017
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Hierarchical Clustering Hinrich Sch¨utze 2014
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Flat Clustering Hinrich Sch¨utze 2014
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Vector Space Classification Hinrich Sch¨utze 2014
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Recap Clustering: Introduction Hinrich Sch¨utze 2014
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Support Vector Machines Hinrich Sch¨utze 2014
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Text Classification & Naive Bayes Hinrich Sch¨utze 2014
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Discriminative Training part 2 Chris Callison-Burch 1155
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Discriminative Training Chris Callison-Burch 1155
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Multi-Class Classification Kai-Wei Chang 2017
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Structured Prediction Models Kai-Wei Chang 2017
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Binary Classification Kai-Wei Chang 2017
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Log-Linear Models Michael Collins, 2018
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Log Linear Models Yejin Choi 2018
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Probability Distributions on Structured Objects Chris Dyer 2015
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Structured Prediction for Language and Other Discrete Data Chris Dyer 2015
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Learning Generative Models Chris Dyer 2015
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Structure and Support Vector Machines Chris Dyer 2015
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Regression for classification Jinho D. Choi 2017
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Classification David Bamman 2017
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Classification 2 David Bamman 2017
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Classification 3 David Bamman 2017
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Features and hypothesis tests David Bamman 2017
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Discriminative Sequence Nathan Schneider 2018
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Linear Models for Classication: Features & Weights Nathan Schneider 2018
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Linear Models for Classication: Discriminative Learning (Perceptron, SVMs, MaxEnt) Nathan Schneider 2018
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Classification Brendan O’Connor 2015
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Logistic Regression Brendan O’Connor 2015
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Log-linear models and CRFs Brendan O’Connor 2015
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CRF and Structured Perceptron Brendan O’Connor 2015
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Text Classification & Linear Models Marine Carpuat 2017
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Logistic Regression & Neural Networks Marine Carpuat 2017
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Naïve Bayes,Perceptron Jacob Eisenstein 2014
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Classication key concepts Jacob Eisenstein 2014
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Semi-Supervised Learning 1 Jacob Eisenstein 2014
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Natural Language Processing: Classification I Dan Klein 2014
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Natural Language Processing Classification II Dan Klein 2014
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Natural Language Processing Classification III Dan Klein 2014
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Lecture 1: Introduction Julia Hockenmaier 2013
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Indian Buffet Process Julia Hockenmaier 2013
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Overview Statistical NLP Tom Kwiatkowski 2016
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Statistical NLP Tom Kwiatkowski 2016
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Text Classification Yoav Artzi 2018
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Classification & Information Theory Lecture #8 Andrew McCallum 2007
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Feature-based Discriminative Models More Sequence Models Yoav Goldberg 2018
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Text Classification Contd + Document Representations Sameer Singh 2017
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Text Classification 1 Sameer Singh 2017
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Lecture 15: Structured Prediction Kevin Gimpel 2017
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Lecture 16: Structured Prediction in NLP, Syntactic & Semantic Formalisms Kevin Gimpel 2017
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Text Clustering Raymond J. Mooney 2017
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Text Categorization Raymond J. Mooney 2017
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Empirical Methods in Natural Language Processing Lecture 12 Philipp Koehn 2008
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Lecture 8: Inference in Structured Prediction Kevin Gimpel 2016
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Lecture 2: Text Classification Kevin Gimpel 2016
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Natural Language Processing (CSEP 517): Text Classication Noah Smith 2017
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CS11-747 Neural Networks for NLP Structured Prediction with Local Dependencies Graham Neubig 2017
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CS11-747 Neural Networks for NLP A Simple (?) Exercise:Predicting the Next Word Graham Neubig 2017
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CS11-747 Neural Networks for NLP Structured Prediction Basics Graham Neubig 2017
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Text Classification and Nave Bayes The Task of Text Classification Dan Jurafsky 2017
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INFORMATION EXTRACTION Trevor Cohn 2018
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TEXT CLASSIFICATION Trevor Cohn 2018
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Lecture 22: Representation Learning Kai-Wei Chang 2016
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Lecture 13: Structured Prediction Kai-Wei Chang 2016
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Title (link) Lecturer Date Votes Error Reviews
Learning: Support Vector Machines Patrick Winston 2014
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Title (link) Author Date Votes Error Reviews
Multi-clustering Datasets Sajib Dasgupta and Vincent Ng 2010
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First Quora Dataset Release: Question Pairs Shankar Iyer, Nikhil Dandekar, Kornél Csernai 2017
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Introduction to Implementing scikit-learn Classifiers Mathew Mayo 2016
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SVMpy Andrew Tulloch 2013
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LIBSVM -- A Library for Support Vector Machines Chih-Jen Lin 2016
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Implementation of the Brown hierarchical word clustering algorithm Percy Liang 2007
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Hierarchical word clustering, following "Brown clustering" Michael Heilman 2013
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fasttext Sale Stock 2017
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DensiTree: making sense of sets of trees Remco Bouckaert 2011
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Planetoid Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov 2016
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Deep Text Classifier Matvey Ezhov 2017
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Text Classification Author Unknown 2017
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Support Vector Machine in Javascript Andrej Karpathy 2017
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Bayesian Unsupervised Topic Segmentation Jacob Eisenstein and Regina Barzilay 2018
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Support Vector Machine (SVM) Explorer Xing Han Lu, Matthew Chan 2018
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Title (link) Author Date Votes Error Reviews
Semi-Supervised Learning and Domain Adaptation in Natural Language Processing Anders Sogaard 2013
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Inductive Semi-supervised Learning with Applicability to NLP Anoop Sarkar and Gholamreza Haffari 2015
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Introduction to Semi-Supervised Learning Xiaojin Zhu and Andrew B. Goldberg 2008
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Learning with Support Vector Machines Colin Campbell and Yiming Ying 2011
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Graph-Based Semi-Supervised Learning Amarnag Subramanya, Partha Pratim Talukdar 2014
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Machine Learning, Neural and Statistical Classification D. Michie, D.J. Spiegelhalter, C.C. Taylor 1994
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A TUTORIAL ON PRINCIPAL COMPONENT ANALYSIS Jon Shlens 2003
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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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Computing with Word Senses Daniel Jurafsky, James H. Martin 2017
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Logistic Regression Daniel Jurafsky, James H. Martin 2017
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Vector Semantics Daniel Jurafsky 2017
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Vector Semantics (Dense Vectors) Daniel Jurafsky 2017
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Title (link) Author Date Votes Error Reviews
Rand index calculation Pakspul 2014
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Support Vector Machine in Javascript Andrej Karpathy 2016
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Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning and Big Data Stefan Kojouharov 2017
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Understanding AI Toolkits Edd Wilder-James 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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GANs are Broken in More than One Way: The Numerics of GANs Ferenc Huszár 2017
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25 Questions to test a Data Scientist on Support Vector Machines Ankit Gupta 2017
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Naive Bayes in Machine Learning Prashant Gupta 2017
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Perceptrons, SVMs and Kernel Methods Daniel Seita 2015
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Logistic Regression Jatin Ahuja 2018
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Expectation Maximization Joydeep Bhattacharjee 2018
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Beyond Accuracy: Precision and Recall William Koehrsen 2018
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Unsupervised Learning Joydeep Bhattacharjee 2018
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Linear Regression?—?Detailed View Saishruthi Swaminathan 2018
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Machine Learning Basics?—?Part 1?—?Concept of Regression Daniel Deutsch 2018
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Machine Learning Basics?—?Part 3?—?Vector Machines, Unsupervised Learning and Principal Component Analysis Daniel Deutsch 2018
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Introduction to k-Nearest-Neighbors Devin Soni 2018
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Recipe: Text clustering using NLTK and scikit-learn George-Bogdan Ivanov 2018
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kmeans text clustering Author Unknown 2018
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Clustering text documents using k-means F Pdregosa 2011
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Document Clustering with Python Brandon Rose 2018
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Clustering text documents using scikit-learn kmeans in Python Various Authors 2018
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Text Mining: how to cluster texts (e.g. news articles) with artificial intelligence? daniel451 2015
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Comparing Python Clustering Algorithms Leland McInnes, John Healy, Steve Astels 2016
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Logistic Regression?—?Detailed Overview Saishruthi Swaminathan 2018
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A Tutorial on Spectral Clustering Chris Ding 2004
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A Tutorial on Spectral Clustering Part 2: Advance/related Topics Chris Ding 2004
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Chapter 1 :Complete Linear Regression with Math. Madhu Sanjeevi 2017
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Chapter 3: Support Vector machine with Math. Madhu Sanjeevi 2017
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Chapter 5 : K-nearest neighbors algorithm with code from scratch. Madhu Sanjeevi 2017
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Chapter 3.1 : SVM from Scratch in Python. Madhu Sanjeevi 2017
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Chapter 2.0 : Logistic Regression with Math. Madhu Sanjeevi 2017
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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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Chapter 2.1: Code for logistic regression with Gradient descent (from scratch and also using Scikit Learn ). Madhu Sanjeevi 2017
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How to do text classification with CNNs, TensorFlow and word embedding Lak Lakshmanan 2017
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Introduction to Bayesian Linear Regression William Koehrsen 1158
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Multi Label Text Classification with Scikit-Learn Susan Li 2018
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An Introduction to Clustering and different methods of clustering Saurav Kaushik 2016
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Unsupervised Learning with Python Vihar Kurama 2018
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Supervised Learning with Python Vihar Kurama 2018
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Ordinary Least Squares Regression Victor Powell, Lewis Lehe 2018
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Principal Component Analysis Victor Powell, Lewis Lehe 2018
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Logistic and Softmax Regression Xianxu Hou 2015
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Simple Softmax Regression in Python?—?Tutorial Arthur Juliani 2016
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Difference Between Softmax Function and Sigmoid Function Saimadhu Polamuri 2017
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Linear Regression Simplified - Ordinary Least Square vs Gradient Descent Prabhu 2018
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Recipe: Text classification using NLTK and scikit-learn bogdani 2016
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Recipe: Text clustering using NLTK and scikit-learn bogdani 2016
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Recipe: Text classification using NLTK and scikit-learn bogdani 2016
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Introducing state of the art text classification with universal language models Jeremy Howard, Sebastian Ruder 2018
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Unsupervised Learning with Python Vihar Kurama 2018
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An Overview of Proxy-label Approaches for Semi-supervised Learning Sebastian Ruder 2018
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Multi Label Text Classification with Scikit-Learn Susan Li 2018
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Building A Logistic Regression in Python, Step by Step Susan Li 2017
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Back-Propagation is very simple. Who made it Complicated ? Prakash Jay 2017
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A Step by Step Backpropagation Example Matt Mazur 2015
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Backpropagation through a fully-connected layer Eli Bendersky 2018
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Learning Backpropagation from Geoffrey Hinton Ryan Gotesman 2018
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Improving Language Understanding with Unsupervised Learning Alec Radford 2018
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Introduction to Pseudo-Labelling : A Semi-Supervised learning technique Shubham Jain 2017
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Clustering Based Unsupervised Learning Syed Sadat Nazrul 2018
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Sent2Vec: An unsupervised approach towards learning sentence embeddings Prerna Kashyap 2018
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K-Means Clustering Seema Singh 2018
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Supervised Learning cheatsheet Shervine Amidi 2018
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Unsupervised Learning cheatsheet Shervine Amidi 2018
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Text Classification is Your New Secret Weapon Adam Geitgey 2018
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Improving Linear Models Using Explicit Kernel Methods Author Unknown 2018
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Text classification with movie reviews Author Unknown 2018
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Generative Adversarial Nets in TensorFlow Agustinus Kristiadi 2016
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Text Classification & Word Representations using FastText (An NLP library by Facebook) NSS 2017
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Complete tutorial on Text Classification using Conditional Random Fields Model (in Python) Pranav Dar 2018
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Linear Discriminant Analysis Srishti Sawla 2018
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Text mining on the command line Sabber Ahamed 2018
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Text Classification Author Unknown 2018
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Multi-Class Text Classification with Doc2Vec & Logistic Regression Susan Li 2018
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K-means Clustering: Algorithm, Applications, Evaluation Methods, and Drawbacks Imad Dabbura 2018
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