Regression
|
Jordan Boyd-Graber |
2021 |
-
0
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|
Report |
Linear regression
|
Carlos Fernandez-Granda |
2019 |
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0
+
|
Report |
Linear Regression
|
Carlos Fernandez-Granda |
2019 |
-
0
+
|
Report |
Review on Regularization for Linear Regression
|
Carlos Fernandez-Granda |
2019 |
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0
+
|
Report |
Sparse regression
|
Carlos Fernandez-Granda |
2019 |
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0
+
|
Report |
Sparse regression
|
Carlos Fernandez-Granda |
2019 |
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0
+
|
Report |
DEEP GENERATIVE MODEL
|
Rose Yu |
2020 |
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0
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|
Report |
Generative Models
|
Rose Yu |
2020 |
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0
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|
Report |
Normalizing Flow Models
|
Rose Yu |
2020 |
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0
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|
Report |
GENERATIVE ADVERSARIAL NETWORK
|
Rose Yu |
2020 |
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0
+
|
Report |
ENERGY-BASED MODELS
|
Rose Yu |
2020 |
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0
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|
Report |
EVALUATION OF GENERATIVE MODELS
|
Rose Yu |
2020 |
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0
+
|
Report |
Backpropagation
|
Ryan Cotterell |
2021 |
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0
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|
Report |
Alpha-beta pruning
|
Chelsea Finn, Nima Anari |
2021 |
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0
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|
Report |
Classification
|
Chelsea Finn, Nima Anari |
2021 |
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0
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|
Report |
Recurrences, Dynamic Programming
|
Chelsea Finn, Nima Anari |
2021 |
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0
+
|
Report |
Regression
|
Chelsea Finn, Nima Anari |
2021 |
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0
+
|
Report |
Stochastic gradient descent
|
Chelsea Finn, Nima Anari |
2021 |
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0
+
|
Report |
Unsupervised learning
|
Chelsea Finn, Nima Anari |
2021 |
-
0
+
|
Report |
Bayesian Decision Theory
|
Kristian Kersting |
2020 |
-
0
+
|
Report |
Clustering and Evaluation
|
Kristian Kersting |
2020 |
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0
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|
Report |
Regression
|
Kristian Kersting |
2020 |
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0
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|
Report |
Classification
|
Kristian Kersting |
2020 |
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0
+
|
Report |
Support Vector Machines
|
Kristian Kersting |
2020 |
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0
+
|
Report |
Kernel Regression and Gaussian Processes
|
Kristian Kersting |
2020 |
-
0
+
|
Report |
Text Classification (I): Logistic Regression
|
Yangfeng Ji |
2021 |
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0
+
|
Report |
Text Classification: A further discussion
|
Yangfeng Ji |
2021 |
-
0
+
|
Report |
Clustering Algorithms
|
Yangfeng Ji |
2021 |
-
0
+
|
Report |
Generative Modeling
|
Yangfeng Ji |
2021 |
-
0
+
|
Report |
CS168: The Modern Algorithmic Toolbox Lecture #7: Understanding and Using Principal Component Analysis (PCA)
|
Tim Roughgarden, Gregory Valiant |
2020 |
-
0
+
|
Report |
Generative Models
|
David Duvenaud |
2018 |
-
0
+
|
Report |
Generative Models II
|
Phillip Isola |
2018 |
-
0
+
|
Report |
Generative and Discriminative Learning
|
Gerard de Melo |
2018 |
-
0
+
|
Report |
Logistic Regression
|
Ansaf Salleb-Aouissi' |
2019 |
-
0
+
|
Report |
SVM
|
Ansaf Salleb-Aouissi' |
2019 |
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0
+
|
Report |
Supervised Learning Methods k-nearest-neighbors(k-NN) Decisiontrees(Chapter18.3) Neuralnetworks(ANN) Supportvectormachines(SVM)
|
Chuck Dyer' |
2019 |
-
0
+
|
Report |
Multiclass Logistic Regression
|
David McAllester' |
2019 |
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0
+
|
Report |
Geometry and Nearest Neighbors
|
Furong Huang' |
2019 |
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0
+
|
Report |
Binary Classification with Linear Models
|
Furong Huang' |
2019 |
-
0
+
|
Report |
Unsupervised Learning
|
John Paisley' |
2019 |
-
0
+
|
Report |
Feature Expansions
|
John Paisley' |
2019 |
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0
+
|
Report |
Least Squares Continued
|
John Paisley' |
2019 |
-
0
+
|
Report |
Linear Regression
|
John Paisley' |
2019 |
-
0
+
|
Report |
Minimum L2 Regression
|
John Paisley' |
2019 |
-
0
+
|
Report |
Soft Clustering vs Hard Clustering
|
John Paisley' |
2019 |
-
0
+
|
Report |
Classification
|
John Paisley' |
2019 |
-
0
+
|
Report |
k-Nearest Neighbors
|
Razvan C. Bunescu' |
2019 |
-
0
+
|
Report |
The Perceptron
|
Razvan C. Bunescu' |
2019 |
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0
+
|
Report |
Machine Learning: Logistic Regression
|
Razvan C. Bunescu' |
2019 |
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0
+
|
Report |
Clustering
|
Ethem Alpaydın' |
2019 |
-
0
+
|
Report |
Supervised Learning
|
Ethem Alpaydın' |
2019 |
-
0
+
|
Report |
Kernel Methods
|
Ethem Alpaydın' |
2019 |
-
0
+
|
Report |
Linear Discrimination
|
Ethem Alpaydın' |
2019 |
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0
+
|
Report |
Multi-layer perceptrons
|
Ethem Alpaydın' |
2019 |
-
0
+
|
Report |
Introduction to Bayesian Linear Regression, Model Comparison and Selection
|
Nicholas Zabaras' |
2019 |
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0
+
|
Report |
Kernel Methods and Introduction to Gaussian Processes
|
Nicholas Zabaras' |
2019 |
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0
+
|
Report |
Bayesian Linear Regression (continued)
|
Nicholas Zabaras' |
2019 |
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0
+
|
Report |
Bayesian Regression
|
Nicholas Zabaras' |
2019 |
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0
+
|
Report |
Unsupervised learning (part1)
|
David Sontag' |
2019 |
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0
+
|
Report |
Hierarchical & Spectral clustering
|
David Sontag' |
2019 |
-
0
+
|
Report |
Clustering
|
David Sontag' |
2019 |
-
0
+
|
Report |
Support vector machines (SVMs) Lecture 3
|
David Sontag' |
2019 |
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0
+
|
Report |
Support Vector Machines:Introduction
|
Ansaf Salleb-Aouissi' |
2019 |
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0
+
|
Report |
Support Vector Machines: Kernels
|
Ansaf Salleb-Aouissi' |
2019 |
-
0
+
|
Report |
Linear classification: Logistic Regression
|
Ansaf Salleb-Aouissi' |
2019 |
-
0
+
|
Report |
K-Means, GMMs and EM
|
Ansaf Salleb-Aouissi' |
2019 |
-
0
+
|
Report |
Support Vector Machines: primal, dual forms and soft-margin
|
Ansaf Salleb-Aouissi' |
2019 |
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0
+
|
Report |
Maximum Likelihood and Gaussian Models
|
Ansaf Salleb-Aouissi' |
2019 |
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0
+
|
Report |
Naive Bayes Classifier
|
Ansaf Salleb-Aouissi' |
2019 |
-
0
+
|
Report |
Linear Regression, Ridge regression, and Lasso
|
Ansaf Salleb-Aouissi' |
2019 |
-
0
+
|
Report |
Tree Classifiers (Decision Trees)
|
Ansaf Salleb-Aouissi' |
2019 |
-
0
+
|
Report |
Linear Classification: Perceptron
|
Ansaf Salleb-Aouissi' |
2019 |
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0
+
|
Report |
Naive Bayes: Text Classification
|
Ansaf Salleb-Aouissi' |
2019 |
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0
+
|
Report |
Linear Regression, Least Squares and Gradient Descent
|
Ansaf Salleb-Aouissi' |
2019 |
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0
+
|
Report |
Support vector machines (SVMs)
|
Matt Gormley' |
2019 |
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0
+
|
Report |
Logistic Regression, Nonlinear Features, Regularization
|
Matt Gormley' |
2019 |
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0
+
|
Report |
Text Classification 1
|
Christopher Manning' |
2019 |
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0
+
|
Report |
Language Identification and Naïve Bayes
|
Wei Xu |
2019 |
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0
+
|
Report |
Intro to Supervised Learning: KNN
|
Sebastian Raschka |
2019 |
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0
+
|
Report |
Logistic Regression and Naive Bayes
|
Jordan Boyd-Graber |
2019 |
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0
+
|
Report |
Logistic Regression and Naive Bayes
|
Jordan Boyd-Graber |
2019 |
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0
+
|
Report |
Logistic Regression and Naive Bayes
|
Jordan Boyd-Graber |
2019 |
-
0
+
|
Report |
Classification and Feature Engineering
|
Jordan Boyd-Graber |
2019 |
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0
+
|
Report |
Classification and Feature Engineering
|
Jordan Boyd-Graber |
2019 |
-
0
+
|
Report |
Classification and Feature Engineering
|
Jordan Boyd-Graber |
2019 |
-
0
+
|
Report |
Linear Models
|
Roger Grosse and Jimmy Ba |
2019 |
-
0
+
|
Report |
Multilayer Perceptrons
|
Roger Grosse and Jimmy Ba |
2019 |
-
0
+
|
Report |
Kernel Methods
|
Mark Schmidt |
2017 |
-
0
+
|
Report |
Log-Linear Models
|
Mark Schmidt |
2017 |
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0
+
|
Report |
Multi-class Classification
|
Greg Durrett |
2017 |
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0
+
|
Report |
Trees
|
Greg Durrett |
2017 |
-
0
+
|
Report |
Trees
|
Greg Durrett |
2017 |
-
0
+
|
Report |
Logistic regression
|
Anna Rogers |
2019 |
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0
+
|
Report |
Linear Regression
|
Matus Telgarsky |
2022 |
-
0
+
|
Report |
Logistic Regression
|
Matus Telgarsky |
2022 |
-
0
+
|
Report |
Linear prediction: features, overfitting, and losses
|
Matus Telgarsky |
2022 |
-
0
+
|
Report |
Support Vector Machine
|
Matus Telgarsky |
2022 |
-
0
+
|
Report |
k-means
|
Matus Telgarsky |
2019 |
-
0
+
|
Report |
Logistic Regression and Gradient Descent
|
M. Magdon-Ismail |
2022 |
-
0
+
|
Report |
Linear text classification
|
Emma Strubell |
2020 |
-
0
+
|
Report |
Nonlinear text classification
|
Emma Strubell |
2020 |
-
0
+
|
Report |
Text classification
|
David Bamman |
2021 |
-
0
+
|
Report |
Text classification
|
David Bamman |
2021 |
-
0
+
|
Report |
Text classification
|
David Bamman |
2021 |
-
0
+
|
Report |
Text classification
|
David Bamman |
2021 |
-
0
+
|
Report |
Text Classification
|
Graham Neubig |
2021 |
-
0
+
|
Report |
Classification
|
Alan W Black and David Mortensen |
2021 |
-
0
+
|
Report |
Classification
|
Alan W Black and David Mortensen |
2021 |
-
0
+
|
Report |
UNSUPERVISED LEARNING
|
Angelica Sun |
2021 |
-
0
+
|
Report |
Classification and Regression
|
Angelica Sun |
2021 |
-
0
+
|
Report |
Generative Learning Algorithms
|
Angelica Sun |
2021 |
-
0
+
|
Report |
Kernel Methods
|
Angelica Sun |
2021 |
-
0
+
|
Report |
Lecture #5: Generalization
|
Tim Roughgarden, Gregory Valiant |
2021 |
-
0
+
|
Report |
Lecture #6: Regularization
|
Tim Roughgarden, Gregory Valiant |
2021 |
-
0
+
|
Report |
Decision trees
|
Sanjoy Dasgupta |
2020 |
-
0
+
|
Report |
Kernel methods
|
Sanjoy Dasgupta |
2020 |
-
0
+
|
Report |
Logistic regression
|
Sanjoy Dasgupta |
2020 |
-
0
+
|
Report |
Multiclass linear prediction
|
Sanjoy Dasgupta |
2020 |
-
0
+
|
Report |
Nearest neighbor classification
|
Sanjoy Dasgupta |
2020 |
-
0
+
|
Report |
A simple linear classifier
|
Sanjoy Dasgupta |
2020 |
-
0
+
|
Report |
Random forests
|
Sanjoy Dasgupta |
2020 |
-
0
+
|
Report |
Linear regression
|
Sanjoy Dasgupta |
2020 |
-
0
+
|
Report |
Lecture 7: Generative Models and Expectation-Maximization
|
Constantinos Daskalakis |
2018 |
-
0
+
|
Report |
Backprop Examples
|
Katherine Keith |
2020 |
-
0
+
|
Report |
Lecture 11: KNN and Decision Trees
|
Katherine Keith |
2020 |
-
0
+
|
Report |
Multi-Class Classification
|
Katherine Keith |
2020 |
-
0
+
|
Report |
K-Nearest Neighbors and Decision Trees
|
Katherine Keith |
2020 |
-
0
+
|
Report |
Kernel Trick
|
Katherine Keith |
2020 |
-
0
+
|
Report |
Support Vector Machines
|
Katherine Keith |
2020 |
-
0
+
|
Report |
Overview of Unsupervised Learning and K-Means Clustering
|
Katherine Keith |
2020 |
-
0
+
|
Report |
Bayesian Classification
|
Katherine Keith |
2020 |
-
0
+
|
Report |
Linear Regression
|
Katherine Keith |
2020 |
-
0
+
|
Report |
Gradient Descent
|
Katherine Keith |
2020 |
-
0
+
|
Report |
Multivariate Linear Regression
|
Katherine Keith |
2020 |
-
0
+
|
Report |
Normal equations and vectorized gradient descent
|
Katherine Keith |
2020 |
-
0
+
|
Report |
Logistic Regression
|
Katherine Keith |
2020 |
-
0
+
|
Report |
The Perceptron
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
The Perceptron
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Linear Classification and Regression
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Linear Classification and Regression
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Logistic Regression and Gradient Descent
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Nonlinear Transforms
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Nonlinear Transforms
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Similarity and Nearest Neighbor
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Similarity and Nearest Neighbor
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Memory and Efficiency in Nearest Neighbor
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Memory and Efficiency in Nearest Neighbor
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Radial Basis Functions
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Radial Basis Functions
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
A Peek At Unsupervised Learning
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
A Peek At Unsupervised Learning
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Multilayer Perceptron
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Multilayer Perceptron
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Neural Networks: Backpropagation
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Neural Networks: Backpropagation
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Neural Networks and Overfitting
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Neural Networks and Overfitting
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
SVM’s: Maximizing the Margin
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
SVM’s: Maximizing the Margin
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
The Kernel Trick
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
The Kernel Trick
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Kernel Machines
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Kernel Machines
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Learning Aides
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Learning Aides
|
Malik Magdon-Ismail |
2020 |
-
0
+
|
Report |
Back-Propagation: From Fully Connected to Convolutional Layers
|
Shuiwang Ji, Yaochen Xie |
2019 |
-
0
+
|
Report |
Back-Propagation: From Fully Connected to Convolutional Layers
|
Shuiwang Ji, Yaochen Xie |
2019 |
-
0
+
|
Report |
Logistic Regression: From Binary to Multi-Class
|
Shuiwang Ji, Yaochen Xie |
2019 |
-
0
+
|
Report |
Logistic Regression: From Binary to Multi-Class
|
Shuiwang Ji, Yaochen Xie |
2019 |
-
0
+
|
Report |
A Neural Network View of Kernel Methods
|
Shuiwang Ji, Yaochen Xie |
2019 |
-
0
+
|
Report |
Classification
|
Greg Durrett |
2021 |
-
0
+
|
Report |
Multiclass
|
Greg Durrett |
2021 |
-
0
+
|
Report |
Multiclass Examples
|
Greg Durrett |
2021 |
-
0
+
|
Report |
Multiclass Classification
|
Greg Durrett |
2021 |
-
0
+
|
Report |
Connections between Perceptron and Logistic Regression
|
Greg Durrett |
2021 |
-
0
+
|
Report |
Linear Discriminant Functions, Perceptron
|
Erkut Erdem |
2021 |
-
0
+
|
Report |
Multi-layer Perceptron, Forward Pass
|
Erkut Erdem |
2021 |
-
0
+
|
Report |
Support Vector Machines
|
Erkut Erdem |
2021 |
-
0
+
|
Report |
Soft Margin Classification, Multi-class SVMs, Kernels
|
Erkut Erdem |
2021 |
-
0
+
|
Report |
Kernel Trick for SVMs, Risk and Loss, Support Vector Regression
|
Erkut Erdem |
2021 |
-
0
+
|
Report |
Decision Trees
|
Erkut Erdem |
2021 |
-
0
+
|
Report |
Clustering, K-Means
|
Erkut Erdem |
2021 |
-
0
+
|
Report |
Clustering
|
Erkut Erdem |
2021 |
-
0
+
|
Report |
Nearest Neighbor Classifier
|
Erkut Erdem |
2021 |
-
0
+
|
Report |
Logistic Regression, Discriminative vs. Generative Classification
|
Erkut Erdem |
2021 |
-
0
+
|
Report |
Classification
|
Trevor Hastie and Robert Tibshirani |
2020 |
-
0
+
|
Report |
Linear Regressioin
|
Trevor Hastie and Robert Tibshirani |
2020 |
-
0
+
|
Report |
SVM
|
Trevor Hastie and Robert Tibshirani |
2020 |
-
0
+
|
Report |
Tree-based Methods
|
Trevor Hastie and Robert Tibshirani |
2020 |
-
0
+
|
Report |
Unsupervised Learning
|
Trevor Hastie and Robert Tibshirani |
2020 |
-
0
+
|
Report |
Stocastic Gradient Descent for Logistic Regression
|
Jordan Boyd-Graber |
2021 |
-
0
+
|
Report |
Logistic Regression
|
Ansaf Salleb-Aouissi' |
2019 |
-
0
+
|
Report |
SVM
|
Ansaf Salleb-Aouissi' |
2019 |
-
0
+
|
Report |
Tensor Methods for Feature Learning
|
Anima Anandkumar |
2019 |
-
0
+
|
Report |
Overview Statistical NLP'
|
Tom Kwiatkowski' |
2016 |
-
0
+
|
Report |
Statistical NLP'
|
Tom Kwiatkowski' |
2016 |
-
0
+
|
Report |
CS11-747 Neural Networks for NLP Structured Prediction with Local Dependencies'
|
Graham Neubig' |
2017 |
-
0
+
|
Report |
CS11-747 Neural Networks for NLP Structured Prediction Basics'
|
Graham Neubig' |
2017 |
-
0
+
|
Report |
Lecture 22: Representation Learning'
|
Kai-Wei Chang' |
2016 |
-
0
+
|
Report |
Lecture 13: Structured Prediction'
|
Kai-Wei Chang' |
2016 |
-
0
+
|
Report |
“Why Should I Trust You?” Explaining the Predictions of Any Classifier
|
Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin |
2016 |
-
0
+
|
Report |
Unsupervised Learning, Representation, and Generative Models
|
Giorgio Patrini |
2017 |
-
0
+
|
Report |
Unsupervised Learning
|
Giorgio Patrini |
2018 |
-
0
+
|
Report |
Classification and kNN 1
|
Dragomir Radev |
2017 |
-
0
+
|
Report |
Classification and kNN 2
|
Dragomir Radev |
2017 |
-
0
+
|
Report |
Generative and Discriminative Models 1
|
Dragomir Radev |
2017 |
-
0
+
|
Report |
Perceptron
|
Dragomir Radev |
2017 |
-
0
+
|
Report |
Logistic Regression 1
|
Dragomir Radev |
2017 |
-
0
+
|
Report |
Examples of Text Classification
|
Dragomir Radev |
2016 |
-
0
+
|
Report |
Feature Selection
|
Dragomir Radev |
2017 |
-
0
+
|
Report |
Evaluation of Text Classification
|
Dragomir Radev |
2016 |
-
0
+
|
Report |
Practical Considerations of Classification
|
Dragomir Radev |
2016 |
-
0
+
|
Report |
Learning: Support Vector Machines
|
Patrick Winston |
2014 |
-
0
+
|
Report |
Hilbert Space Embeddings of Distributions
|
Eric Xing |
2014 |
-
0
+
|
Report |
Word/Sentence Embeddings, Text Classification
|
Mohit Bansal |
2017 |
-
0
+
|
Report |
Logistic Regression
|
Marine Carpuat |
2018 |
-
0
+
|
Report |
Logistic regression, Loss Functions, Neural Networks
|
Marine Carpuat |
2018 |
-
0
+
|
Report |
Structured Perceptron, Viterbi
|
Marine Carpuat |
2018 |
-
0
+
|
Report |
Loss-augmented Structured Prediction
|
Marine Carpuat |
2018 |
-
0
+
|
Report |
Multiclass Classification
|
Dan Roth |
2017 |
-
0
+
|
Report |
Multiclass Classification
|
Dan Roth |
2017 |
-
0
+
|
Report |
Global and Local Views
|
Dan Roth |
2017 |
-
0
+
|
Report |
Structured Prediction Basics
|
Dan Roth |
2017 |
-
0
+
|
Report |
SVMs
|
Dan Roth |
2017 |
-
0
+
|
Report |
Guiding Semi-Supervision with Constraint-Driven Learning
|
Dan Roth |
2017 |
-
0
+
|
Report |
Binary classification
|
Greg Durrett |
2018 |
-
0
+
|
Report |
Multiclass classification
|
Greg Durrett |
2018 |
-
0
+
|
Report |
Unsupervised Learning
|
Greg Durrett |
2018 |
-
0
+
|
Report |
The perceptron
|
François Fleuret |
2019 |
-
0
+
|
Report |
Probabilistic view of a linear classifier
|
François Fleuret |
2019 |
-
0
+
|
Report |
Linear separability and feature design
|
François Fleuret |
2019 |
-
0
+
|
Report |
Multi-Layer Perceptrons
|
François Fleuret |
2019 |
-
0
+
|
Report |
Evaluation
|
Ralph Grishman |
2018 |
-
0
+
|
Report |
Maximum Entropy Models and Feature Engineering
|
Ralph Grishman |
2018 |
-
0
+
|
Report |
Meta-Learning
|
Sergey Levine |
2018 |
-
0
+
|
Report |
Statistical learning, kNN, linear classifiers
|
Svetlana Lazebnik |
2018 |
-
0
+
|
Report |
Linear classifiers part I
|
Svetlana Lazebnik |
2018 |
-
0
+
|
Report |
Linear classifiers part II
|
Svetlana Lazebnik |
2018 |
-
0
+
|
Report |
Linear models review
|
Svetlana Lazebnik |
2018 |
-
0
+
|
Report |
Multi-class classification
|
Svetlana Lazebnik |
2018 |
-
0
+
|
Report |
Nearest Neighbor Methods
|
Sebastian Raschka |
2018 |
-
0
+
|
Report |
Decision Trees
|
Sebastian Raschka |
2018 |
-
0
+
|
Report |
Evaluation Methods
|
Sebastian Raschka |
2018 |
-
0
+
|
Report |
Evaluation Methods
|
Sebastian Raschka |
2018 |
-
0
+
|
Report |
Evaluation Methods
|
Sebastian Raschka |
2018 |
-
0
+
|
Report |
Evaluation Methods
|
Sebastian Raschka |
2018 |
-
0
+
|
Report |
Evaluation Methods
|
Sebastian Raschka |
2018 |
-
0
+
|
Report |
Feature Selection
|
Sebastian Raschka |
2018 |
-
0
+
|
Report |
Feature Selection
|
Sebastian Raschka |
2018 |
-
0
+
|
Report |
Discriminative Models: MaxEnt,Perceptron
|
Ruihong Huang |
2017 |
-
0
+
|
Report |
Semi-supervised Learning for NLP
|
Richard Socher |
2018 |
-
0
+
|
Report |
Supervised Linear Regression
|
Xavier Bresso |
2017 |
-
0
+
|
Report |
Supervised Classification
|
Xavier Bresso |
2017 |
-
0
+
|
Report |
Neural Nets (wrap-up) and Decision Trees
|
Pieter Abbeel, Dan Klein |
2018 |
-
0
+
|
Report |
Generative and Discriminative Learning
|
Vivek Srikumar |
2018 |
-
0
+
|
Report |
Machine Learning (classification)
|
Alan Ritter |
2019 |
-
0
+
|
Report |
Dirichlet-Multinomial + Naive Bayes
|
Alan Ritter |
2017 |
-
0
+
|
Report |
Linear Regression
|
Alan Ritter |
2017 |
-
0
+
|
Report |
Logistic Regression
|
Alan Ritter |
2017 |
-
0
+
|
Report |
Perceptron
|
Alan Ritter |
2017 |
-
0
+
|
Report |
Kernel Methods
|
Alan Ritter |
2017 |
-
0
+
|
Report |
SVMs
|
Alan Ritter |
2017 |
-
0
+
|
Report |
Discriminant Functions
|
Sargur N. Srihari |
2018 |
-
0
+
|
Report |
Probabilistic Generative Models
|
Sargur N. Srihari |
2018 |
-
0
+
|
Report |
Fixed Basis Functions
|
Sargur N. Srihari |
2018 |
-
0
+
|
Report |
Multiclass Logistic Regression
|
Sargur N. Srihari |
2018 |
-
0
+
|
Report |
Probit Regression
|
Sargur N. Srihari |
2018 |
-
0
+
|
Report |
Bayesian Logistic Regression
|
Sargur N. Srihari |
2018 |
-
0
+
|
Report |
Variational Bayesian Logistic Regression
|
Sargur N. Srihari |
2018 |
-
0
+
|
Report |
Kernel Methods
|
Sargur N. Srihari |
2018 |
-
0
+
|
Report |
Radial Basis Function Networks
|
Sargur N. Srihari |
2018 |
-
0
+
|
Report |
Gaussian Processes
|
Sargur N. Srihari |
2018 |
-
0
+
|
Report |
Unsupervised Learning
|
Sargur N. Srihari |
2018 |
-
0
+
|
Report |
Introduction to Predictive Models and kNN
|
Mladen Kolar and Rob McCulloch |
2015 |
-
0
+
|
Report |
Classification
|
Mladen Kolar and Rob McCulloch |
2015 |
-
0
+
|
Report |
Perceptron
|
Mladen Kolar and Rob McCulloch |
2015 |
-
0
+
|
Report |
Logistic regression
|
Mladen Kolar and Rob McCulloch |
2015 |
-
0
+
|
Report |
Generative Models
|
Justin Johnson, Serena Yeung, Fei-Fei Li |
2019 |
-
0
+
|
Report |
Support vector machines (SVMs).
|
Mehryar Mohri |
2018 |
-
0
+
|
Report |
Kernel methods
|
Mehryar Mohri |
2018 |
-
0
+
|
Report |
Multi-class classification
|
Mehryar Mohri |
2018 |
-
0
+
|
Report |
Regression
|
Mehryar Mohri |
2018 |
-
0
+
|
Report |
Overview of Linguistics
|
Nathan Schneider |
2019 |
-
0
+
|
Report |
Classification: naïve Bayes; Noisy Channel Model
|
Nathan Schneider |
2019 |
-
0
+
|
Report |
Linear models for classification: features, weights
|
Nathan Schneider |
2019 |
-
0
+
|
Report |
Linear models for classification: discriminative learning (perceptron, SVMs, MaxEnt)
|
Nathan Schneider |
2019 |
-
0
+
|
Report |
Linear Models 1
|
Peter Bloem |
2019 |
-
0
+
|
Report |
Linear Models 2
|
Peter Bloem |
2019 |
-
0
+
|
Report |
Perceptrons
|
Graham Neubig |
2015 |
-
0
+
|
Report |
Advanced discriminative models
|
Graham Neubig |
2015 |
-
0
+
|
Report |
The structured perceptron
|
Graham Neubig |
2015 |
-
0
+
|
Report |
Shallow NLP: Naive Bayes and MaxEnt for text classification
|
Alex Lascarides, Shay Cohen |
2019 |
-
0
+
|
Report |
Generative and Discriminative Models 1
|
Dragomir Radev |
2018 |
-
0
+
|
Report |
Clustering 1
|
Dragomir Radev |
2018 |
-
0
+
|
Report |
Examples of Text Classification
|
Dragomir Radev |
2018 |
-
0
+
|
Report |
Introduction to Support Vector Machines and Kernels
|
Dragomir Radev |
2018 |
-
0
+
|
Report |
Practical Considerations of Classification
|
Dragomir Radev |
2018 |
-
0
+
|
Report |
Classification and kNN 1
|
Dragomir Radev |
2018 |
-
0
+
|
Report |
Logistic Regression 1
|
Dragomir Radev |
2018 |
-
0
+
|
Report |
Perceptron
|
Dragomir Radev |
2018 |
-
0
+
|
Report |
Classification and kNN 2
|
Dragomir Radev |
2018 |
-
0
+
|
Report |
Feature Selection
|
Dragomir Radev |
2018 |
-
0
+
|
Report |
Evaluation of Text Classification
|
Dragomir Radev |
2018 |
-
0
+
|
Report |
Unsupervised Learning
|
George Konidaris |
2016 |
-
0
+
|
Report |
Kernels and Clustering
|
Dan Klein and Pieter Abbeel |
2014 |
-
0
+
|
Report |
Bayes Nets
|
Dan Klein and Pieter Abbeel |
2014 |
-
0
+
|
Report |
Constraint Satisfaction Problems II
|
Dan Klein and Pieter Abbeel |
2014 |
-
0
+
|
Report |
Perceptrons
|
Dan Klein and Pieter Abbeel |
2014 |
-
0
+
|
Report |
Supervised Learning Methods k-nearest-neighbors(k-NN) Decisiontrees(Chapter18.3) Neuralnetworks(ANN) Supportvectormachines(SVM)
|
Chuck Dyer |
2018 |
-
0
+
|
Report |
Supervised Learning Methods k-nearest-neighbors (k-NN) Decision trees Support vector machines (SVM) Neural networks
|
Chuck Dyer |
2018 |
-
0
+
|
Report |
Multiclass Logistic Regression
|
David McAllester |
2017 |
-
0
+
|
Report |
Geometry and Nearest Neighbors
|
Furong Huang |
2018 |
-
0
+
|
Report |
The Perceptron
|
Furong Huang |
2018 |
-
0
+
|
Report |
K-Means Clustering (Unsupervised Learning)
|
Furong Huang |
2018 |
-
0
+
|
Report |
The Perceptron
|
Furong Huang |
2018 |
-
0
+
|
Report |
Multiclass Classification and Reductions
|
Furong Huang |
2018 |
-
0
+
|
Report |
Binary Classification with Linear Models
|
Furong Huang |
2018 |
-
0
+
|
Report |
Unsupervised Learning
|
John Paisley |
2017 |
-
0
+
|
Report |
Maximum Margin Classifiers
|
John Paisley |
2017 |
-
0
+
|
Report |
Feature Expansions
|
John Paisley |
2017 |
-
0
+
|
Report |
Linear Classification
|
John Paisley |
2017 |
-
0
+
|
Report |
Least Squares Continued
|
John Paisley |
2017 |
-
0
+
|
Report |
Linear Regression
|
John Paisley |
2017 |
-
0
+
|
Report |
Minimum L2 Regression
|
John Paisley |
2017 |
-
0
+
|
Report |
Soft Clustering vs Hard Clustering
|
John Paisley |
2017 |
-
0
+
|
Report |
Classification
|
John Paisley |
2017 |
-
0
+
|
Report |
k-Nearest Neighbors
|
Razvan C. Bunescu |
2017 |
-
0
+
|
Report |
The Perceptron
|
Razvan C. Bunescu |
2017 |
-
0
+
|
Report |
Machine Learning: Logistic Regression
|
Razvan C. Bunescu |
2017 |
-
0
+
|
Report |
Clustering
|
Ethem Alpaydın |
2014 |
-
0
+
|
Report |
Supervised Learning
|
Ethem Alpaydın |
2014 |
-
0
+
|
Report |
Kernel Methods
|
Ethem Alpaydın |
2014 |
-
0
+
|
Report |
Linear Discrimination
|
Ethem Alpaydın |
2014 |
-
0
+
|
Report |
Multi-layer perceptrons
|
Ethem Alpaydın |
2014 |
-
0
+
|
Report |
Introduction to Bayesian Linear Regression, Model Comparison and Selection
|
Nicholas Zabaras |
2017 |
-
0
+
|
Report |
Kernel Methods and Introduction to Gaussian Processes
|
Nicholas Zabaras |
2017 |
-
0
+
|
Report |
Gaussian Processes for Classification Problems, Course Summary
|
Nicholas Zabaras |
2017 |
-
0
+
|
Report |
Bayesian Linear Regression (continued)
|
Nicholas Zabaras |
2017 |
-
0
+
|
Report |
Bayesian Regression
|
Nicholas Zabaras |
2017 |
-
0
+
|
Report |
Unsupervised learning (part1)
|
David Sontag |
2016 |
-
0
+
|
Report |
Hierarchical & Spectral clustering
|
David Sontag |
2016 |
-
0
+
|
Report |
Clustering
|
David Sontag |
2016 |
-
0
+
|
Report |
Support vector machines (SVMs) Lecture 3
|
David Sontag |
2016 |
-
0
+
|
Report |
Support vector machines (SVMs)
|
David Sontag |
2016 |
-
0
+
|
Report |
Support vector machines (SVMs)
|
David Sontag |
2016 |
-
0
+
|
Report |
Support vector machines (SVMs)
|
David Sontag |
2016 |
-
0
+
|
Report |
Mixture Modeling
|
Roger Grosse |
2018 |
-
0
+
|
Report |
Gaussian Naive Bayes
|
Matt Gormley |
2016 |
-
0
+
|
Report |
Machine Learning in Practice + k-Nearest Neighbors
|
Matt Gormley |
2016 |
-
0
+
|
Report |
K-Means and GMMs
|
Matt Gormley |
2016 |
-
0
+
|
Report |
k-Nearest Neighbors
|
Matt Gormley |
2016 |
-
0
+
|
Report |
Clustering(K-Means)
|
Matt Gormley |
2016 |
-
0
+
|
Report |
Naive Bayes
|
Tom M. Mitchell |
2015 |
-
0
+
|
Report |
Support Vector Machines (SVMs)
|
Maria-Florina Balcan |
2015 |
-
0
+
|
Report |
Semi-Supervised Learning
|
Maria-Florina Balcan |
2015 |
-
0
+
|
Report |
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 |
-
0
+
|
Report |
Logistic regression and Generative/Discriminative classifiers
|
Tom M. Mitchell |
2015 |
-
0
+
|
Report |
Linear regression
|
Tom M. Mitchell |
2015 |
-
0
+
|
Report |
Kernels Methods in Machine Learning
|
Maria-Florina Balcan |
2015 |
-
0
+
|
Report |
10-601 Machine Learning Maria-Florina Balcan
|
Maria-Florina Balcan |
2015 |
-
0
+
|
Report |
Decision Trees
|
Tom M. Mitchell |
2015 |
-
0
+
|
Report |
Linear regression
|
Tom M. Mitchell |
2015 |
-
0
+
|
Report |
Logistic regression and Generative/Discriminative classifiers
|
Tom M. Mitchell |
2015 |
-
0
+
|
Report |
Decision Trees
|
Dan Roth |
2018 |
-
0
+
|
Report |
Perceptron
|
Dan Roth |
2018 |
-
0
+
|
Report |
Support Vector Machines
|
Dan Roth |
2018 |
-
0
+
|
Report |
Classification with Scattering
|
Joan Bruna |
2016 |
-
0
+
|
Report |
Seperable Scattering Operators
|
Joan Bruna |
2016 |
-
0
+
|
Report |
Latent Graphical Models
|
Joan Bruna |
2016 |
-
0
+
|
Report |
Transformation Groups
|
Joan Bruna |
2016 |
-
0
+
|
Report |
Random Forests
|
Joan Bruna |
2016 |
-
0
+
|
Report |
Local invariants and convolution
|
Joan Bruna |
2016 |
-
0
+
|
Report |
Classification, kernels and metrics
|
Joan Bruna |
2016 |
-
0
+
|
Report |
Text Classification 1
|
Christopher Manning |
2017 |
-
0
+
|
Report |
SVMs
|
Christopher Manning |
2017 |
-
0
+
|
Report |
SVMs
|
Christopher Manning |
2017 |
-
0
+
|
Report |
Hierarchical Clustering
|
Hinrich Sch¨utze |
2014 |
-
0
+
|
Report |
Flat Clustering
|
Hinrich Sch¨utze |
2014 |
-
0
+
|
Report |
Vector Space Classification
|
Hinrich Sch¨utze |
2014 |
-
0
+
|
Report |
Recap Clustering: Introduction
|
Hinrich Sch¨utze |
2014 |
-
0
+
|
Report |
Support Vector Machines
|
Hinrich Sch¨utze |
2014 |
-
0
+
|
Report |
Text Classification & Naive Bayes
|
Hinrich Sch¨utze |
2014 |
-
0
+
|
Report |
Discriminative Training part 2
|
Chris Callison-Burch |
1155 |
-
0
+
|
Report |
Discriminative Training
|
Chris Callison-Burch |
1155 |
-
0
+
|
Report |
Multi-Class Classification
|
Kai-Wei Chang |
2017 |
-
0
+
|
Report |
Structured Prediction Models
|
Kai-Wei Chang |
2017 |
-
0
+
|
Report |
Binary Classification
|
Kai-Wei Chang |
2017 |
-
0
+
|
Report |
Log-Linear Models
|
Michael Collins, |
2018 |
-
0
+
|
Report |
Log Linear Models
|
Yejin Choi |
2018 |
-
0
+
|
Report |
Probability Distributions on Structured Objects
|
Chris Dyer |
2015 |
-
0
+
|
Report |
Structured Prediction for Language and Other Discrete Data
|
Chris Dyer |
2015 |
-
0
+
|
Report |
Learning Generative Models
|
Chris Dyer |
2015 |
-
0
+
|
Report |
Structure and Support Vector Machines
|
Chris Dyer |
2015 |
-
0
+
|
Report |
Classification
|
David Bamman |
2017 |
-
0
+
|
Report |
Classification 2
|
David Bamman |
2017 |
-
0
+
|
Report |
Classification 3
|
David Bamman |
2017 |
-
0
+
|
Report |
Features and hypothesis tests
|
David Bamman |
2017 |
-
0
+
|
Report |
Discriminative Sequence
|
Nathan Schneider |
2018 |
-
0
+
|
Report |
Linear Models for Classication: Features & Weights
|
Nathan Schneider |
2018 |
-
0
+
|
Report |
Linear Models for Classication: Discriminative Learning (Perceptron, SVMs, MaxEnt)
|
Nathan Schneider |
2018 |
-
0
+
|
Report |
Classification
|
Brendan O?Connor |
2015 |
-
0
+
|
Report |
Logistic Regression
|
Brendan O?Connor |
2015 |
-
0
+
|
Report |
Log-linear models and CRFs
|
Brendan O?Connor |
2015 |
-
0
+
|
Report |
CRF and Structured Perceptron
|
Brendan O?Connor |
2015 |
-
0
+
|
Report |
Text Classification & Linear Models
|
Marine Carpuat |
2017 |
-
0
+
|
Report |
Logistic Regression & Neural Networks
|
Marine Carpuat |
2017 |
-
0
+
|
Report |
Naïve Bayes,Perceptron
|
Jacob Eisenstein |
2014 |
-
0
+
|
Report |
Classication key concepts
|
Jacob Eisenstein |
2014 |
-
0
+
|
Report |
Semi-Supervised Learning 1
|
Jacob Eisenstein |
2014 |
-
0
+
|
Report |
Natural Language Processing: Classification I
|
Dan Klein |
2014 |
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0
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Report |
Natural Language Processing Classification II
|
Dan Klein |
2014 |
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0
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Report |
Natural Language Processing Classification III
|
Dan Klein |
2014 |
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0
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Report |
Lecture 1: Introduction
|
Julia Hockenmaier |
2013 |
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0
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Report |
Indian Buffet Process
|
Julia Hockenmaier |
2013 |
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0
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Report |
Text Classification
|
Yoav Artzi |
2018 |
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0
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Report |
Classification & Information Theory Lecture #8
|
Andrew McCallum |
2007 |
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0
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Report |
Feature-based Discriminative Models More Sequence Models
|
Yoav Goldberg |
2018 |
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0
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Report |
Text Classification Contd + Document Representations
|
Sameer Singh |
2017 |
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0
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Report |
Text Classification 1
|
Sameer Singh |
2017 |
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0
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Report |
Lecture 15: Structured Prediction
|
Kevin Gimpel |
2017 |
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0
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Report |
Lecture 16: Structured Prediction in NLP, Syntactic & Semantic Formalisms
|
Kevin Gimpel |
2017 |
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0
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Report |
Text Clustering
|
Raymond J. Mooney |
2017 |
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0
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Report |
Text Categorization
|
Raymond J. Mooney |
2017 |
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0
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Report |
Empirical Methods in Natural Language Processing Lecture 12
|
Philipp Koehn |
2008 |
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0
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Lecture 8: Inference in Structured Prediction
|
Kevin Gimpel |
2016 |
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0
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Report |
Lecture 2: Text Classification
|
Kevin Gimpel |
2016 |
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0
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Report |
Natural Language Processing (CSEP 517): Text Classication
|
Noah Smith |
2017 |
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0
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Report |
CS11-747 Neural Networks for NLP A Simple (?) Exercise:Predicting the Next Word
|
Graham Neubig |
2017 |
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0
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Report |
Text Classification and Nave Bayes The Task of Text Classification
|
Dan Jurafsky |
2017 |
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0
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Report |
INFORMATION EXTRACTION
|
Trevor Cohn |
2018 |
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0
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Report |
TEXT CLASSIFICATION
|
Trevor Cohn |
2018 |
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0
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Report |
Semi-Supervised Learning
|
Jacob Eisenstein |
2017 |
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0
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Report |
Encoder-Decoder Neural Networks
|
Nal Kalchbrenner |
2017 |
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0
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Report |
Mathematics for Machine Learning: Classification with Support Vector Machines
|
Marc Peter Deisenroth, A Aldo Faisal, Cheng Soon Ong |
2018 |
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0
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Report |
VIP Cheatsheet: Supervised Learning
|
Afshine Amidi and Shervine Amidi |
2018 |
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0
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Report |
VIP Cheatsheet: Unsupervised Learning
|
Afshine Amidi and Shervine Amidi |
2018 |
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0
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Report |