转自：爱可可-爱生活

机器学习综述、教程和书籍的阅读清单包括：主动学习、生物信息学、分类、聚类、计算机视觉、深度学习、多视图学习、自然语言处理、概率模型、推荐系统、加强学习、机器人、半监督学习、无监督学习。

A curated list of Machine Learning related Surveys, Overviews and Books.

If you want to contribute to this list (please do!), check How to Contribute wiki or contact me @MeTroFuN.

Table of Contents

• Active Learning

• Bioinformatics

• Classification

• Clustering

• Computer Vision

• Deep Learning

• Dimensionality Reduction

• Ensemble Learning

• Multi-Armed Bandit

• Multi-View Learning

• Natural Language Processing

• Probabilistic Models

• Recommender Systems

• Reinforcement Learning

• Robotics

• Semi-Supervised Learning

• Submodular Functions

• Transfer Learning

• Unsupervised Learning

Active Learning

• Active Learning Literature Survey (2010) [B Settles] [67pp]

Bioinformatics

• Introduction to Bioinformatics (2013) [A Lesk] [255pp]

• Bioinformatics - an Introduction for Computer Scientists (2004) [J Cohen] [37pp]

Classification

• Supervised Machine Learning: A Review of Classification Techniques(2007) [SB Kotsiantis, I Zaharakis, P Pintelas] [20pp]

• Web Page Classification: Features and Algorithms (2009) [X Qi, BD Davison] [31pp]

Clustering

• Data Clustering: 50 Years Beyond K-Means (2010) [AK Jain] [16pp]  ⭐

• A Tutorial on Spectral Clustering (2007) [U VON Luxburg] [32pp]

• Handbook of Blind Source Separation: Independent Component Analysis and Applications (2010) [P Comon, C Jutten] [65pp]

• Survey of Clustering Algorithms (2005) [R Xu, D Wunsch] [34pp]

• A Survey of Clustering Data Mining Techniques (2006) [P Berkhin] [56pp]

• Clustering (2008) [R Xu, D Wunsch] [341pp]

Computer Vision

• Pedestrian Detection: An Evaluation of the State of the Art (2012) [P Dollar, C Wojek, B Schiele] [19pp]  ⭐

• Computer Vision: Algorithms and Applications (2010) [R Szeliski] [874pp]  ⭐

• A Survey of Appearance Models in Visual Object Tracking (2013) [X Li] [42pp]  ⭐

• Object Tracking: A Survey (2006) [A Yilmaz] [45pp]

• Head Pose Estimation in Computer Vision: A Survey (2009) [E Murphy-chutorian, MM Trivedi] [20pp]

• A Survey of Recent Advances in Face Detection (2010) [C Zhang, Z Zhang] [17pp]

• Monocular Model-Based 3d Tracking of Rigid Objects: A Survey (2005) [V Lepetit] [91pp]

• A Survey on Face Detection in the Wild: Past, Present and Future(2015) [S Zafeiriou, C Zhang, Z Zhang] [50pp]

Deep Learning

• Deep Learning (2016) [IJ Goodfellow, Y Bengio, A Courville] [800pp]  ⭐⭐

• Deep Learning in Neural Networks: An Overview (2015) [J Schmidhuber] [88pp]  ⭐⭐

• Learning Deep Architectures for Ai (2009) [Y Bengio] [71pp]  ⭐

• Tutorial on Variational Autoencoders (2016) [C Doersch] [65pp]  ⭐

• Deep Reinforcement Learning: An Overview (2017) [ Y Li] [30pp]

• NIPS 2016 Tutorial: Generative Adversarial Networks (2016) [I Goodfellow] [57pp]

Dimensionality Reduction

• Dimensionality Reduction: A Comparative Review (2009) [L VAN DER Maaten, E Postma] [36pp]

• Dimension Reduction: A Guided Tour (2010) [CJC Burges] [64pp]

Ensemble Learning

• Ensemble Methods: Foundations and Algorithms (2012) [ZH Zhou] [234pp]

• Ensemble Approaches for Regression: A Survey (2012) [J Mendes-moreira, C Soares, AM Jorge] [40pp]

Multi-Armed Bandit

• Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems (2012) [S Bubeck, N Cesa-bianchi] [130pp]  ⭐

• A Survey of Online Experiment Design With the Stochastic Multi-Armed Bandit (2015) [G Burtini, J Loeppky, R Lawrence] [49pp]

Multi-View Learning

• A Survey on Multi-View Learning (2013) [C Xu] [59pp]

• A Survey of Multi-View Machine Learning (2013) [S Sun] [13pp]

Natural Language Processing

• A Primer on Neural Network Models for Natural Language Processing(2016) [Y Goldberg] [76pp]  ⭐

• Probabilistic Topic Models (2012) [DM Blei] [16pp]  ⭐

• Natural Language Processing (Almost) From Scratch (2011) [R Collobert] [45pp]  ⭐

• Opinion Mining and Sentiment Analysis (2008) [B Pang, L Lee] [94pp]  ⭐

• Opinion Mining and Sentiment Analysis (2012) [B Liu, L Zhang] [38pp]

• Machine Learning in Automated Text Categorization (2002) [F Sebastiani] [55pp]

• Statistical Machine Translation (2009) [P Koehn] [149pp]

• Statistical Machine Translation (2008) [A Lopez] [55pp]

• Machine Transliteration Survey (2011) [S Karimi, F Scholer, A Turpin] [46pp]

• Neural Machine Translation and Sequence-to-Sequence Models: A Tutorial (2017) [GRAHAM Neubig] [57pp]

Probabilistic Models

• Graphical Models, Exponential Families, and Variational Inference(2008) [MJ Wainwright, MI Jordan] [305pp]

• An Introduction to Conditional Random Fields (2011) [C Sutton] [90pp]

• An Introduction to Conditional Random Fields for Relational Learning(2006) [C Sutton] [35pp]

• An Introduction to Mcmc for Machine Learning (2003) [C Andrieu, N DE Freitas, A Doucet, MI Jordan] [39pp]

• Introduction to Probability Models (2014) [SM Ross] [801pp]

Recommender Systems

• Introduction to Recommender Systems Handbook (2011) [F Ricci, L Rokach, B Shapira] [845pp]  ⭐

• Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions (2008) [G Adomavicius, A Tuzhilin] [43pp]  ⭐

• Matrix Factorization Techniques for Recommender Systems (2009) [Y Koren, R Bell, C Volinsky] [8pp]  ⭐

• A Survey of Collaborative Filtering Techniques (2009) [X Su, TM Khoshgoftaar] [20pp]

Reinforcement Learning

• Reinforcement Learning in Robotics: A Survey (2013) [J Kober, JA Bagnell, J Peterskober] [74pp]  ⭐

• Deep Reinforcement Learning: An Overview (2017) [ Y Li] [30pp]

• Reinforcement Learning: An Introduction (2016) [RS Sutton, AG Barto] [398pp]

• Bayesian Reinforcement Learning: A Survey (2016) [M Ghavamzadeh, S Mannor, J Pineau] [147pp]

• Reinforcement Learning: A Survey (1996) [LP Kaelbling, ML Littman, AW Moore] [49pp]

Robotics

• Reinforcement Learning in Robotics: A Survey (2013) [J Kober, JA Bagnell, J Peterskober] [74pp]  ⭐

• A Survey of Robot Learning From Demonstration (2009) [BD Argall, S Chernova, M Veloso] [15pp]

Semi-Supervised Learning

• Semi-Supervised Learning Literature Survey (2008) [X Zhu] [59pp]

Submodular Functions

• Learning With Submodular Functions: A Convex Optimization Perspective (2013) [F Bach] [173pp]

• Submodular Function Maximization (2012) [A Krause, D Golovin] [28pp]

Transfer Learning

• A Survey on Transfer Learning (2010) [SJ Pan, Q Yang] [15pp]  ⭐

• Transfer Learning for Reinforcement Learning Domains: A Survey(2009) [ME Taylor, P Stone] [53pp]

Unsupervised Learning

• Tutorial on Variational Autoencoders (2016) [C Doersch] [65pp] 

   

链接；

https://github.com/metrofun/machine-learning-surveys/

原文链接：

http://weibo.com/5501429448/F5XPpdMMo?ref=home&rid=6_0_202_2606587605829542820&type=comment#_rnd1496399539712