Topic 5: Classification 717

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Title (link) Author Date Votes Error
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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Text Classification Algorithms: A Survey Kamran Kowsari 2019
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Understanding Neural Networks via Feature Visualization: A survey "Anh Nguyen, Jason Yosinski, and Jeff Clune" 2019
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Text Classification Algorithms: A Survey Kamran Kowsari 2019
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A Survey of Text Classification Algorithms Charu C. Aggarwal, ChengXiang Zhai 2012
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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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Machine Learning, Neural and Statistical Classification D. Michie, D.J. Spiegelhalter, C.C. Taylor 1994
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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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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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Naive Bayes and Text Classification Sebastian Raschka 2014
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Unsupervised Learning Zoubin Ghahramani 2005
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The Automatic Statistician Christian Steinruecken, Emma Smith, David Janz, James Lloyd, Zoubin Ghahramani 2018
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Title (link) Author Date Votes Error
K-Nearest Neighbors Demo Andrej Karpathy 2017
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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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Dealing With Class Imbalanced Datasets For Classification. Aditya Lahiri 2018
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Text Classification with State of the Art NLP Library — Flair Tadej Magajna 2018
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Naive Classification using Matrix Dot Product / Change of Basis with Interactive Code in Numpy Jae Duk Seo 2018
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Demystifying Maths of SVM Krishna Kumar Mahto 2019
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Logistic Regression: a quick introduction Irene Li 2015
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Beginning SVM from Scratch in Python Harrison 2018
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Words as Features NLTK Tutorial Harrison 2018
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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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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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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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Backpropagation through a fully-connected layer Eli Bendersky 2018
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Rand index calculation Pakspul 2014
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Data Science Simplified Part 11: Logistic Regression Pradeep Menon 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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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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Implementing a Principal Component Analysis (PCA) Sebastian Raschka 2014
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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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Understanding Feature Engineering (Part 2)???Categorical Data Dipajan Sarkar 2018
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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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Generalization and Equilbrium in Generative Adversarial Nets (GANs) Sanjeev Arora 2017
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Learning Backpropagation from Geoffrey Hinton Ryan Gotesman 2018
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Automated Feature Engineering in Python William Koehrsen 2018
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Open Machine Learning Course. Topic 3. Classification, Decision Trees and k Nearest Neighbors Yury Kashnitskiy 2018
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Open Machine Learning Course. Topic 6. Feature Engineering and Feature Selection Arseny Kravchenko 2018
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Open Machine Learning Course. Topic 7. Unsupervised Learning: PCA and Clustering Sergey Korolev 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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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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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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Machine Learning: An In-Depth Guide - Unsupervised Learning, Related Fields, and Machine Learning in Practice Alex Castrounis 2018
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Machine Learning for Humans, Part 3: Unsupervised Learnin Vishal Maini 2017
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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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Comprehensive Support Vector Machines Guide - Using Illusion to Solve Reality! Pranov Mishra 2018
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Taxonomy Learning Sebastian Ruder 2018
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Text classification Sebastian Ruder 2018
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5 Reasons “Logistic Regression” should be the first thing you learn when becoming a Data Scientist Dima Shulga 2018
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Flow-based Deep Generative Models Lilian Weng 2018
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GANs beyond generation: 7 alternative use cases Alexandr Honchar 2018
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The Perceptron Matt Mazur 2014
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Report on Text Classification using CNN, RNN & HAN Akshat Maheshwari 2018
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Text Classification using Algorithms gk_ 2017
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Naive Bayes and Text Classification Sebastian Raschka 2014
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Multi-Class Text Classification Model Comparison and Selection 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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Generative-Discriminative Pairs Kwan-Yuet (Stephen) Ho 2016
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Classification – Logistic Regression Paresh Sharma 2018
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Deep Generative Models: A Unified Statistical View Petuum, Inc. 2018
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A different kind of (deep) learning: part 1 Gidi Shperber 2018
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Title (link) Author Date Votes Error
Deep Generative Models Ishaan Gulrajani 2017
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Deep Generative Models Aaron Courville 2018
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Deep Generative Models Alexander Amini 2019
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NLP Programming Tutorial 3 - The Perceptron Algorithm Graham Neubig 2015
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NLP Programming Tutorial 6 - Advanced Discriminative Learning Graham Neubig 2015
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NLP Programming Tutorial 11 - The Structured Perceptron Graham Neubig 2015
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Deep Generative Models Ava Soleimany 2021
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Machine Learning with Kernel Methods Julien Mairal & Jean-Philippe Vert 2017
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Generative Models Shakir Mohamed 2018
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Neural Semi-supervised Learning under Domain Shift Sebastian Ruder 2018
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Tree Based Methods InFoCusp Innovations Pvt. Ltd. 2018
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Generative Models InFoCusp Innovations Pvt. Ltd. 2018
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Linear Regression, Logistic Regression InFoCusp Innovations Pvt. Ltd. 2018
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Naive Bayes Classifier InFoCusp Innovations Pvt. Ltd. 2018
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Support Vector Machines InFoCusp Innovations Pvt. Ltd. 2018
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Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python Prateek Joshi 2018
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Multi-Class Text Classification with Scikit-Learn Susan Li 2018
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A Guide to using Logistic Regression for Digit Recognition (with Python codes) Srikar 2018
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Three Popular Clustering Methods and When to Use Each #ODSC - The Open Data Science Community 2018
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A Leap into the Future: Generative Adversarial Networks Matt Hergot 2019
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5 powerful Scikit-Learn Examples Luke Posey 2019
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A gentle introduction to text summarization Alfrick Opidi 2019
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Intuitively understanding variatonal autoencoders Irhum Shafkat 2019
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Variational Autoencoders Explained in detail Yoel Zeldes 2019
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Novel Methods for Text Generation using Adverserial Learning and Autoencoders Mariya Yao 2019
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Unsupervised Deep Learning "Alex Graves, Marc'Aurelio Ranzato" 2018
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Thoughts on progress made and challenges ahead in few-shot learning Hugo Larochelle 2018
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ML algorithms Draft V1.0 Irene Lizihui 2019
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Active Learning for Natural Language Processing Shilpa Arora and Sachin Agarwal 2007
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Neural Semi-supervised Learning Alex Fabbri 2018
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Transfer Learning Irene Li 2018
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Generative models in Tensorflow 2 Tim Sainburg 2019
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Combination of Abstractive & Extractive methods for Text Summarization (Tutorial 7) amr zaki 2019
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Approaches to Text Summarization: An Overview Matthew Mayo 2019
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Introducing FastBert\\\A simple Deep Learning library for BERT Models Kaushal Trivedi 2019
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Wasserstein GAN James Allingham 2019
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Building a Multi-label Text Classifier using BERT and TensorFlow Javaid Nabi 2019
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NLP Learning Series: Part 2\\\Conventional Methods for Text Classification Rahul Agarwal 2019
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Transfer Learning Intuition for Text Classification Rahul Agarwal 2019
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The Perceptron\\\A Building Block of Neural Networks Ani Karenovna 2019
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Everything You Wanted to Know about the Kernel Trick Eric Kim 2017
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Kernel Functions for Machine Learning Applications César Souza 2010
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Kernel Support Vector Machines for Classification and Regression in C# César Souza 2010
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Kernel Principal Component Analysis in C# César Souza 2010
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A Beginner\s Guide to Hierarchical Clustering and how to Perform it in Python Pulkit Sharma 2019
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Classification Sebastian Ruder 2019
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Transfer Learning in NLP Desh Raj 2018
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The Complete Guide to Decision Trees (part 1) Diego Lopez Yse 2019
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The Complete Guide to Decision Trees (part 2) Diego Lopez Yse 2019
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The Anatomy of K-Means Clustering Diego Lopez Yse 2019
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Feature Engineering in Python Diva Jain 2019
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Accuracy: from classification to clustering evaluation Stanislas Morbieu 2019
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Improving Language Understanding with Unsupervised Learning Alec Radford 2018
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BERT to the rescue! Dima Shulga 2019
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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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Text Classifier Algorithms in Machine Learning Roman Trusov 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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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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Unsupervised Machine Learning: Hierarchical Clustering Harrison 2018
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Regression - Intro and Data Harrison 2018
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Regression - Features and Labels Harrison 2018
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Support Vector Machine introduction Harrison 2018
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Kernels Introduction Harrison 2018
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Text Classification NLTK Tutorial Harrison 2018
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Naive Bayes Classifier with NLTK Harrison 2018
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Combining Classification Algorithms with NLTK Harrison 2018
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Improving Training Data for sentiment analysis with NLTK Harrison 2018
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Representing and comparing probabilities with kernels: Part 1 Arthur Gretton 2018
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The Success of Deep Generative Models Jakub Tomczak 2018
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NIPS 2016 Tutorial: Generative Adversarial Networks Ian Goodfellow 2016
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Learning with Latent Linguistic Structure Junxian He, Pengcheng Yin, Chunting Zhou, Taylor Berg-Kirkpatrick 2018
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Text Quantification: A Tutorial Fabrizio Sebastiani 2014
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Towards Open-domain Generation of Programs from Natural Language Graham Neubig 2018
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Tutorial on Deep Generative Models Shakir Mohamed, Danilo Rezende 2017
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Classification, logistic regression Suriya Gunasekar 2018
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On Logistic Regression: Gradients of the Log Loss, Multi-Class Classification, and Other Optimization Techniques Karl Stratos 2018
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Classification, support vector machines Suriya Gunasekar 2018
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SVMs: Non-Separable Data, Convex Surrogate Loss, Multi-Class Classification, Kernels Karl Stratos 2018
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Generative Models, structured classification Suriya Gunasekar 2018
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EM for Naive Bayes and Gaussian Mixture Models, k-Means Clustering Karl Stratos 2018
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Linear Regression in 2 Minutes (using PyTorch) Sanyam Bhutani 2018
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Large Scale Spectral Clustering with Landmark-Based Representation (in Julia) Int8 2016
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A Comprehensive Guide to Understand and Implement Text Classification in Python Shivam Bansal 2018
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An Introduction to Clustering and different methods of clustering Saurav Kaushik 2016
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Deep Transfer Learning for Natural Language Processing — Text Classification with Universal Embeddings Dipanjan (DJ) Sarkar 2018
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Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python Prateek Joshi 2018
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Building a Logistic Regression in Python, Step by Step Susan Li 2017
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A TUTORIAL ON PRINCIPAL COMPONENT ANALYSIS Jon Shlens 2003
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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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Introduction to Classification: Likelihoods, Margins, Features, and Kernels Dan Klein 2007
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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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Semi-supervised Learning Tutorial John Blitzer and Xiaojin Zhu 2008
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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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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 TensorFlow sachin joglekar 2015
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Machine Learning Classics : The Perceptron The Data Science Lab 2014
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Enter the Perceptron Triangle Inequality 2014
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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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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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News Categorization using Multinomial Naive Bayes Andres Soto Villaverde 2016
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SVM Tutorial - Part I Chris McCormick 2013
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Linear Classifiers Andr´e Martins 2021
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Introductory guide to Information Retrieval using kNN and KDTree Gurchetan Singh 2017
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Machine Learning Workflows in Python from Scratch Part 2: k-means Clustering Matthew Mayo 2017
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The Logistic Regression Algorithm Niklas Donges 2019
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Graph-based Semi-supervised Learning Zoubin Ghahramani 2012
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Generalized Linear Models Roger Levy 2012
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Title (link) Lecturer Date Votes Error
Regression Jordan Boyd-Graber 2021
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Linear regression Carlos Fernandez-Granda 2019
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Linear Regression Carlos Fernandez-Granda 2019
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Review on Regularization for Linear Regression Carlos Fernandez-Granda 2019
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Sparse regression Carlos Fernandez-Granda 2019
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Sparse regression Carlos Fernandez-Granda 2019
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DEEP GENERATIVE MODEL Rose Yu 2020
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Generative Models Rose Yu 2020
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Normalizing Flow Models Rose Yu 2020
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GENERATIVE ADVERSARIAL NETWORK Rose Yu 2020
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ENERGY-BASED MODELS Rose Yu 2020
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EVALUATION OF GENERATIVE MODELS Rose Yu 2020
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Backpropagation Ryan Cotterell 2021
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Alpha-beta pruning Chelsea Finn, Nima Anari 2021
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Classification Chelsea Finn, Nima Anari 2021
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Recurrences, Dynamic Programming Chelsea Finn, Nima Anari 2021
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Regression Chelsea Finn, Nima Anari 2021
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Stochastic gradient descent Chelsea Finn, Nima Anari 2021
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Unsupervised learning Chelsea Finn, Nima Anari 2021
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Bayesian Decision Theory Kristian Kersting 2020
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Clustering and Evaluation Kristian Kersting 2020
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Regression Kristian Kersting 2020
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Classification Kristian Kersting 2020
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Support Vector Machines Kristian Kersting 2020
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Kernel Regression and Gaussian Processes Kristian Kersting 2020
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Text Classification (I): Logistic Regression Yangfeng Ji 2021
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Text Classification: A further discussion Yangfeng Ji 2021
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Clustering Algorithms Yangfeng Ji 2021
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Generative Modeling Yangfeng Ji 2021
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CS168: The Modern Algorithmic Toolbox Lecture #7: Understanding and Using Principal Component Analysis (PCA) Tim Roughgarden, Gregory Valiant 2020
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Generative Models David Duvenaud 2018
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Generative Models II Phillip Isola 2018
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Generative and Discriminative Learning Gerard de Melo 2018
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Logistic Regression Ansaf Salleb-Aouissi' 2019
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SVM Ansaf Salleb-Aouissi' 2019
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Supervised Learning Methods k-nearest-neighbors(k-NN) Decisiontrees(Chapter18.3) Neuralnetworks(ANN) Supportvectormachines(SVM) Chuck Dyer' 2019
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Multiclass Logistic Regression David McAllester' 2019
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Geometry and Nearest Neighbors Furong Huang' 2019
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Binary Classification with Linear Models Furong Huang' 2019
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Unsupervised Learning John Paisley' 2019
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Feature Expansions John Paisley' 2019
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Least Squares Continued John Paisley' 2019
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Linear Regression John Paisley' 2019
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Minimum L2 Regression John Paisley' 2019
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Soft Clustering vs Hard Clustering John Paisley' 2019
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Classification John Paisley' 2019
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k-Nearest Neighbors Razvan C. Bunescu' 2019
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The Perceptron Razvan C. Bunescu' 2019
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Machine Learning: Logistic Regression Razvan C. Bunescu' 2019
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Clustering Ethem Alpaydın' 2019
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Supervised Learning Ethem Alpaydın' 2019
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Kernel Methods Ethem Alpaydın' 2019
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Linear Discrimination Ethem Alpaydın' 2019
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Multi-layer perceptrons Ethem Alpaydın' 2019
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Introduction to Bayesian Linear Regression, Model Comparison and Selection Nicholas Zabaras' 2019
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Kernel Methods and Introduction to Gaussian Processes Nicholas Zabaras' 2019
- 0 +
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Bayesian Linear Regression (continued) Nicholas Zabaras' 2019
- 0 +
Report
Bayesian Regression Nicholas Zabaras' 2019
- 0 +
Report
Unsupervised learning (part1) David Sontag' 2019
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Hierarchical & Spectral clustering David Sontag' 2019
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Clustering David Sontag' 2019
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Support vector machines (SVMs) Lecture 3 David Sontag' 2019
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Support Vector Machines:Introduction Ansaf Salleb-Aouissi' 2019
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Support Vector Machines: Kernels Ansaf Salleb-Aouissi' 2019
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Linear classification: Logistic Regression Ansaf Salleb-Aouissi' 2019
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K-Means, GMMs and EM Ansaf Salleb-Aouissi' 2019
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Support Vector Machines: primal, dual forms and soft-margin Ansaf Salleb-Aouissi' 2019
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Maximum Likelihood and Gaussian Models Ansaf Salleb-Aouissi' 2019
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Naive Bayes Classifier Ansaf Salleb-Aouissi' 2019
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Linear Regression, Ridge regression, and Lasso Ansaf Salleb-Aouissi' 2019
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Tree Classifiers (Decision Trees) Ansaf Salleb-Aouissi' 2019
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Linear Classification: Perceptron Ansaf Salleb-Aouissi' 2019
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Naive Bayes: Text Classification Ansaf Salleb-Aouissi' 2019
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Linear Regression, Least Squares and Gradient Descent Ansaf Salleb-Aouissi' 2019
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Support vector machines (SVMs) Matt Gormley' 2019
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Logistic Regression, Nonlinear Features, Regularization Matt Gormley' 2019
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Text Classification 1 Christopher Manning' 2019
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Language Identification and Naïve Bayes Wei Xu 2019
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Intro to Supervised Learning: KNN Sebastian Raschka 2019
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Logistic Regression and Naive Bayes Jordan Boyd-Graber 2019
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Logistic Regression and Naive Bayes Jordan Boyd-Graber 2019
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Logistic Regression and Naive Bayes Jordan Boyd-Graber 2019
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Classification and Feature Engineering Jordan Boyd-Graber 2019
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Classification and Feature Engineering Jordan Boyd-Graber 2019
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Multilayer Perceptrons Roger Grosse and Jimmy Ba 2019
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Kernel Methods Mark Schmidt 2017
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Log-Linear Models Mark Schmidt 2017
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Multi-class Classification Greg Durrett 2017
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Trees Greg Durrett 2017
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Report
Trees Greg Durrett 2017
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Logistic regression Anna Rogers 2019
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Linear Regression Matus Telgarsky 2022
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Logistic Regression Matus Telgarsky 2022
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Linear prediction: features, overfitting, and losses Matus Telgarsky 2022
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Support Vector Machine Matus Telgarsky 2022
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k-means Matus Telgarsky 2019
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Logistic Regression and Gradient Descent M. Magdon-Ismail 2022
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Linear text classification Emma Strubell 2020
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Nonlinear text classification Emma Strubell 2020
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Text classification David Bamman 2021
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Report
Text classification David Bamman 2021
- 0 +
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Text classification David Bamman 2021
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Report
Text classification David Bamman 2021
- 0 +
Report
Text Classification Graham Neubig 2021
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Classification Alan W Black and David Mortensen 2021
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Classification Alan W Black and David Mortensen 2021
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UNSUPERVISED LEARNING Angelica Sun 2021
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Classification and Regression Angelica Sun 2021
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Generative Learning Algorithms Angelica Sun 2021
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Kernel Methods Angelica Sun 2021
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Lecture #5: Generalization Tim Roughgarden, Gregory Valiant 2021
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Lecture #6: Regularization Tim Roughgarden, Gregory Valiant 2021
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Decision trees Sanjoy Dasgupta 2020
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Kernel methods Sanjoy Dasgupta 2020
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Logistic regression Sanjoy Dasgupta 2020
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Multiclass linear prediction Sanjoy Dasgupta 2020
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Nearest neighbor classification Sanjoy Dasgupta 2020
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A simple linear classifier Sanjoy Dasgupta 2020
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Random forests Sanjoy Dasgupta 2020
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Linear regression Sanjoy Dasgupta 2020
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Lecture 7: Generative Models and Expectation-Maximization Constantinos Daskalakis 2018
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Backprop Examples Katherine Keith 2020
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Lecture 11: KNN and Decision Trees Katherine Keith 2020
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Multi-Class Classification Katherine Keith 2020
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K-Nearest Neighbors and Decision Trees Katherine Keith 2020
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Kernel Trick Katherine Keith 2020
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Support Vector Machines Katherine Keith 2020
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Overview of Unsupervised Learning and K-Means Clustering Katherine Keith 2020
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Bayesian Classification Katherine Keith 2020
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Linear Regression Katherine Keith 2020
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Gradient Descent Katherine Keith 2020
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Multivariate Linear Regression Katherine Keith 2020
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Normal equations and vectorized gradient descent Katherine Keith 2020
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Logistic Regression Katherine Keith 2020
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The Perceptron Malik Magdon-Ismail 2020
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The Perceptron Malik Magdon-Ismail 2020
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Linear Classification and Regression Malik Magdon-Ismail 2020
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Logistic Regression and Gradient Descent Malik Magdon-Ismail 2020
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Nonlinear Transforms Malik Magdon-Ismail 2020
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Nonlinear Transforms Malik Magdon-Ismail 2020
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Similarity and Nearest Neighbor Malik Magdon-Ismail 2020
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Similarity and Nearest Neighbor Malik Magdon-Ismail 2020
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Memory and Efficiency in Nearest Neighbor Malik Magdon-Ismail 2020
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Memory and Efficiency in Nearest Neighbor Malik Magdon-Ismail 2020
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Radial Basis Functions Malik Magdon-Ismail 2020
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Radial Basis Functions Malik Magdon-Ismail 2020
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A Peek At Unsupervised Learning Malik Magdon-Ismail 2020
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A Peek At Unsupervised Learning Malik Magdon-Ismail 2020
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Multilayer Perceptron Malik Magdon-Ismail 2020
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Neural Networks: Backpropagation Malik Magdon-Ismail 2020
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Neural Networks and Overfitting Malik Magdon-Ismail 2020
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Neural Networks and Overfitting Malik Magdon-Ismail 2020
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SVM’s: Maximizing the Margin Malik Magdon-Ismail 2020
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SVM’s: Maximizing the Margin Malik Magdon-Ismail 2020
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The Kernel Trick Malik Magdon-Ismail 2020
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Learning Aides Malik Magdon-Ismail 2020
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Back-Propagation: From Fully Connected to Convolutional Layers Shuiwang Ji, Yaochen Xie 2019
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Logistic Regression: From Binary to Multi-Class Shuiwang Ji, Yaochen Xie 2019
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Logistic Regression: From Binary to Multi-Class Shuiwang Ji, Yaochen Xie 2019
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A Neural Network View of Kernel Methods Shuiwang Ji, Yaochen Xie 2019
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Classification Greg Durrett 2021
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Multiclass Greg Durrett 2021
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Multiclass Examples Greg Durrett 2021
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Multiclass Classification Greg Durrett 2021
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Connections between Perceptron and Logistic Regression Greg Durrett 2021
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Linear Discriminant Functions, Perceptron Erkut Erdem 2021
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Multi-layer Perceptron, Forward Pass Erkut Erdem 2021
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Support Vector Machines Erkut Erdem 2021
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Soft Margin Classification, Multi-class SVMs, Kernels Erkut Erdem 2021
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Decision Trees Erkut Erdem 2021
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Clustering, K-Means Erkut Erdem 2021
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Nearest Neighbor Classifier Erkut Erdem 2021
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Logistic Regression, Discriminative vs. Generative Classification Erkut Erdem 2021
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Classification Trevor Hastie and Robert Tibshirani 2020
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Linear Regressioin Trevor Hastie and Robert Tibshirani 2020
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SVM Trevor Hastie and Robert Tibshirani 2020
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Tree-based Methods Trevor Hastie and Robert Tibshirani 2020
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Unsupervised Learning Trevor Hastie and Robert Tibshirani 2020
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Stocastic Gradient Descent for Logistic Regression Jordan Boyd-Graber 2021
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Logistic Regression Ansaf Salleb-Aouissi' 2019
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SVM Ansaf Salleb-Aouissi' 2019
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Tensor Methods for Feature Learning Anima Anandkumar 2019
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Overview Statistical NLP' Tom Kwiatkowski' 2016
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Statistical NLP' Tom Kwiatkowski' 2016
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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 Structured Prediction Basics' Graham Neubig' 2017
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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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“Why Should I Trust You?” Explaining the Predictions of Any Classifier Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin 2016
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Unsupervised Learning, Representation, and Generative Models Giorgio Patrini 2017
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Unsupervised Learning Giorgio Patrini 2018
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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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Hilbert Space Embeddings of Distributions Eric Xing 2014
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Word/Sentence Embeddings, Text Classification Mohit Bansal 2017
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Logistic Regression Marine Carpuat 2018
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Logistic regression, Loss Functions, Neural Networks Marine Carpuat 2018
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Structured Perceptron, Viterbi Marine Carpuat 2018
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Loss-augmented Structured Prediction Marine Carpuat 2018
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Multiclass Classification Dan Roth 2017
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Multiclass Classification Dan Roth 2017
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Global and Local Views Dan Roth 2017
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Structured Prediction Basics Dan Roth 2017
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SVMs Dan Roth 2017
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Report
Guiding Semi-Supervision with Constraint-Driven Learning Dan Roth 2017
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Binary classification Greg Durrett 2018
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Multiclass classification Greg Durrett 2018
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Unsupervised Learning Greg Durrett 2018
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The perceptron François Fleuret 2019
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Probabilistic view of a linear classifier François Fleuret 2019
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Linear separability and feature design François Fleuret 2019
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Multi-Layer Perceptrons François Fleuret 2019
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Evaluation Ralph Grishman 2018
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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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Statistical learning, kNN, linear classifiers Svetlana Lazebnik 2018
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Linear classifiers part I Svetlana Lazebnik 2018
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Linear classifiers part II Svetlana Lazebnik 2018
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Linear models review Svetlana Lazebnik 2018
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Multi-class classification Svetlana Lazebnik 2018
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Nearest Neighbor Methods Sebastian Raschka 2018
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Decision Trees Sebastian Raschka 2018
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Evaluation Methods Sebastian Raschka 2018
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Evaluation Methods Sebastian Raschka 2018
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Report
Evaluation Methods Sebastian Raschka 2018
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Report
Evaluation Methods Sebastian Raschka 2018
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Report
Evaluation Methods Sebastian Raschka 2018
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Report
Feature Selection Sebastian Raschka 2018
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Feature Selection Sebastian Raschka 2018
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Discriminative Models: MaxEnt,Perceptron Ruihong Huang 2017
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Semi-supervised Learning for NLP Richard Socher 2018
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Supervised Linear Regression Xavier Bresso 2017
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Supervised Classification Xavier Bresso 2017
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Neural Nets (wrap-up) and Decision Trees Pieter Abbeel, Dan Klein 2018
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Generative and Discriminative Learning Vivek Srikumar 2018
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Machine Learning (classification) Alan Ritter 2019
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Dirichlet-Multinomial + Naive Bayes Alan Ritter 2017
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Linear Regression Alan Ritter 2017
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Logistic Regression Alan Ritter 2017
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Perceptron Alan Ritter 2017
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Kernel Methods Alan Ritter 2017
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SVMs Alan Ritter 2017
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Discriminant Functions Sargur N. Srihari 2018
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Probabilistic Generative Models Sargur N. Srihari 2018
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Fixed Basis Functions Sargur N. Srihari 2018
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Multiclass Logistic Regression Sargur N. Srihari 2018
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Probit Regression Sargur N. Srihari 2018
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Bayesian Logistic Regression Sargur N. Srihari 2018
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Variational Bayesian Logistic Regression Sargur N. Srihari 2018
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Kernel Methods Sargur N. Srihari 2018
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Radial Basis Function Networks Sargur N. Srihari 2018
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Gaussian Processes Sargur N. Srihari 2018
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Unsupervised Learning Sargur N. Srihari 2018
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Report
Introduction to Predictive Models and kNN Mladen Kolar and Rob McCulloch 2015
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Classification Mladen Kolar and Rob McCulloch 2015
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Report
Perceptron Mladen Kolar and Rob McCulloch 2015
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Logistic regression Mladen Kolar and Rob McCulloch 2015
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Generative Models Justin Johnson, Serena Yeung, Fei-Fei Li 2019
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Kernel methods Mehryar Mohri 2018
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Multi-class classification Mehryar Mohri 2018
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Regression Mehryar Mohri 2018
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Report
Overview of Linguistics Nathan Schneider 2019
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Classification: naïve Bayes; Noisy Channel Model Nathan Schneider 2019
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Linear models for classification: features, weights Nathan Schneider 2019
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Linear models for classification: discriminative learning (perceptron, SVMs, MaxEnt) Nathan Schneider 2019
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Linear Models 1 Peter Bloem 2019
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Linear Models 2 Peter Bloem 2019
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Perceptrons Graham Neubig 2015
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Advanced discriminative models Graham Neubig 2015
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The structured perceptron Graham Neubig 2015
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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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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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Report
Constraint Satisfaction Problems II Dan Klein and Pieter Abbeel 2014
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Report
Perceptrons Dan Klein and Pieter Abbeel 2014
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Report
Supervised Learning Methods k-nearest-neighbors(k-NN) Decisiontrees(Chapter18.3) Neuralnetworks(ANN) Supportvectormachines(SVM) Chuck Dyer 2018
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Report
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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Report
Linear Regression John Paisley 2017
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Report
Minimum L2 Regression John Paisley 2017
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Report
Soft Clustering vs Hard Clustering John Paisley 2017
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Report
Classification John Paisley 2017
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Report
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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Report
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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Report
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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Mixture Modeling Roger Grosse 2018
- 0 +
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Gaussian Naive Bayes Matt Gormley 2016
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Report
Machine Learning in Practice + k-Nearest Neighbors Matt Gormley 2016
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K-Means and GMMs Matt Gormley 2016
- 0 +
Report
k-Nearest Neighbors Matt Gormley 2016
- 0 +
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Clustering(K-Means) Matt Gormley 2016
- 0 +
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Naive Bayes Tom M. Mitchell 2015
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Support Vector Machines (SVMs) Maria-Florina Balcan 2015
- 0 +
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Semi-Supervised Learning Maria-Florina Balcan 2015
- 0 +
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Math Review and Decision Trees Maria-Florina Balcan 2015
- 0 +
Report
Naive Bayes Tom M. Mitchell 2015
- 0 +
Report
Clustering. Unsupervised Learning Maria-Florina Balcan 2015
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Report
Logistic regression and Generative/Discriminative classifiers Tom M. Mitchell 2015
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Linear regression Tom M. Mitchell 2015
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Report
Kernels Methods in Machine Learning Maria-Florina Balcan 2015
- 0 +
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10-601 Machine Learning Maria-Florina Balcan Maria-Florina Balcan 2015
- 0 +
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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
- 0 +
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Perceptron Dan Roth 2018
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Report
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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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
- 0 +
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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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Report
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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Report
Log-Linear Models Michael Collins, 2018
- 0 +
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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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Report
Structured Prediction for Language and Other Discrete Data Chris Dyer 2015
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Report
Learning Generative Models Chris Dyer 2015
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Structure and Support Vector Machines Chris Dyer 2015
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Report
Classification David Bamman 2017
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Report
Classification 2 David Bamman 2017
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Report
Classification 3 David Bamman 2017
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Report
Features and hypothesis tests David Bamman 2017
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Report
Discriminative Sequence Nathan Schneider 2018
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Report
Linear Models for Classication: Features & Weights Nathan Schneider 2018
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Report
Linear Models for Classication: Discriminative Learning (Perceptron, SVMs, MaxEnt) Nathan Schneider 2018
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Report
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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Report
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
- 0 +
Report
Classication key concepts Jacob Eisenstein 2014
- 0 +
Report
Semi-Supervised Learning 1 Jacob Eisenstein 2014
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Report
Natural Language Processing: Classification I Dan Klein 2014
- 0 +
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Natural Language Processing Classification II Dan Klein 2014
- 0 +
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Natural Language Processing Classification III Dan Klein 2014
- 0 +
Report
Lecture 1: Introduction Julia Hockenmaier 2013
- 0 +
Report
Indian Buffet Process Julia Hockenmaier 2013
- 0 +
Report
Text Classification Yoav Artzi 2018
- 0 +
Report
Classification & Information Theory Lecture #8 Andrew McCallum 2007
- 0 +
Report
Feature-based Discriminative Models More Sequence Models Yoav Goldberg 2018
- 0 +
Report
Text Classification Contd + Document Representations Sameer Singh 2017
- 0 +
Report
Text Classification 1 Sameer Singh 2017
- 0 +
Report
Lecture 15: Structured Prediction Kevin Gimpel 2017
- 0 +
Report
Lecture 16: Structured Prediction in NLP, Syntactic & Semantic Formalisms Kevin Gimpel 2017
- 0 +
Report
Text Clustering Raymond J. Mooney 2017
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