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General-Machine-Learning

General-Machine-Learning

Readme.md

Papers related to general statistical modeling and classical machine learning i.e. non-deep-learning

Name		Name	Last commit message	Last commit date
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The Hundred-Page Machine Learning Book by Andriy Burkov		The Hundred-Page Machine Learning Book by Andriy Burkov
A Few Useful Things to Know about Machine Learning - Pedro Domingos.pdf		A Few Useful Things to Know about Machine Learning - Pedro Domingos.pdf
A Survey of Collaborative Filtering Techniques.pdf		A Survey of Collaborative Filtering Techniques.pdf
A decision-theoretic generalization of on-line learning and an application to boosting.pdf		A decision-theoretic generalization of on-line learning and an application to boosting.pdf
A survey of cross-validation procedures for model selection.pdf		A survey of cross-validation procedures for model selection.pdf
API design for machine learning software.pdf		API design for machine learning software.pdf
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent.pdf		Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent.pdf
Adaboost - why and how - Jim Matas.pdf		Adaboost - why and how - Jim Matas.pdf
An Efficient Approach for Assessing Hyperparameter Importance.pdf		An Efficient Approach for Assessing Hyperparameter Importance.pdf
An Impossibility Theorem for Clustering - kleinberg-NIPS'15.pdf		An Impossibility Theorem for Clustering - kleinberg-NIPS'15.pdf
An Interactive Machine Learning Framework.pdf		An Interactive Machine Learning Framework.pdf
Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf		Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Boosting the margin- A new explanation for the effectiveness of voting methods.pdf		Boosting the margin- A new explanation for the effectiveness of voting methods.pdf
For valid generalization, the size of the weights is more important than the size of the network.pdf		For valid generalization, the size of the weights is more important than the size of the network.pdf
How to avoid machine learning pitfalls - a guide for academic researchers.pdf		How to avoid machine learning pitfalls - a guide for academic researchers.pdf
Interactive Analysis Needs Theories of Inference.pdf		Interactive Analysis Needs Theories of Inference.pdf
Invariant Risk Minimization ICML'19.pdf		Invariant Risk Minimization ICML'19.pdf
LeonBottouICML2015.pdf		LeonBottouICML2015.pdf
MA Learning_A CriticalSurvey_2003_0516.pdf		MA Learning_A CriticalSurvey_2003_0516.pdf
MBMLbook.rar		MBMLbook.rar
Machine learning at the energy and intensity frontiers of particle physics.pdf		Machine learning at the energy and intensity frontiers of particle physics.pdf
Machine learning testing.pdf		Machine learning testing.pdf
On the effect of data set size on bias and variance in classification learning.pdf		On the effect of data set size on bias and variance in classification learning.pdf
Please Stop Explaining Black Box Models for High-Stakes Decisions.pdf		Please Stop Explaining Black Box Models for High-Stakes Decisions.pdf
Readme.md		Readme.md
Regression Error Characteristic curve (REC).pdf		Regression Error Characteristic curve (REC).pdf
Restructuring Sparse High Dimensional Data for Effective Retrieval - Isbell NIPS 1999.pdf		Restructuring Sparse High Dimensional Data for Effective Retrieval - Isbell NIPS 1999.pdf
Support vectorn networks Vapnik and Cortes paper.pdf		Support vectorn networks Vapnik and Cortes paper.pdf
The Consciousness Prior.pdf		The Consciousness Prior.pdf
The Optimality of Naive Bayes.pdf		The Optimality of Naive Bayes.pdf
The Riemannian Geometry of Deep Generative Models.pdf		The Riemannian Geometry of Deep Generative Models.pdf
Theoretical Impediments to Machine Learning - Judea Pearl.pdf		Theoretical Impediments to Machine Learning - Judea Pearl.pdf
TherML- Thermodynamics of Machine Learning.pdf		TherML- Thermodynamics of Machine Learning.pdf
Top 10 algorithms in data mining.pdf		Top 10 algorithms in data mining.pdf
Underspecification Presents Challenges for Credibility in Modern Machine Learning.pdf		Underspecification Presents Challenges for Credibility in Modern Machine Learning.pdf
Weak vs. Strong Learning and the Adaboost Algorithm.pdf		Weak vs. Strong Learning and the Adaboost Algorithm.pdf
interpretable-machine-learning.pdf		interpretable-machine-learning.pdf

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Papers related to general statistical modeling and classical machine learning i.e. non-deep-learning