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 @ML_Review.

• Active Learning
• Bioinformatics
• Classification
• Clustering
• Computer Vision
• Deep Learning
• Dimensionality Reduction
• Ensemble Learning
• Metric Learning
• Monte Carlo
• Multi-Armed Bandit
• Multi-View Learning
• Natural Language Processing
• Physics
• Probabilistic Models
• Recommender Systems
• Reinforcement Learning
• Robotics
• Semi-Supervised Learning
• Submodular Functions
• Transfer Learning
• Unsupervised Learning

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

• Introduction to Bioinformatics (2013) [A Lesk] [255pp] 📚
• Bioinformatics - an Introduction for Computer Scientists (2004) [J Cohen] [37pp]
• Opportunities and Obstacles for Deep Learning in Biology and Medicine (2017) [T Ching, DS Himmelstein, BK Beaulieu-jones] [102pp]

• 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]

• 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] 📚

• 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]
• A Review on Deep Learning Techniques Applied to Semantic Segmentation (2017) [A Garcia-garcia, S Orts-escolano] [23pp]
• Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017) [D Russo, B VAN Roy, A Kazerouni, I Osband] [67pp]
• Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017) [J Janai, F Güney, A Behl, A Geiger] [14pp]

• 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]
• Opportunities and Obstacles for Deep Learning in Biology and Medicine (2017) [T Ching, DS Himmelstein, BK Beaulieu-jones] [102pp]
• A Review on Deep Learning Techniques Applied to Semantic Segmentation (2017) [A Garcia-garcia, S Orts-escolano] [23pp]
• Deep Learning for Video Game Playing (2017) [N Justesen, P Bontrager, J Togelius, S Risi] [16pp]
• Deep Learning Techniques for Music Generation (2017) [JP Briot, G Hadjeres, F PACHET ] [108pp]

• Dimensionality Reduction: A Comparative Review (2009) [L VAN DER Maaten, E Postma] [36pp]
• Dimension Reduction: A Guided Tour (2010) [CJC Burges] [64pp]

• Ensemble Methods: Foundations and Algorithms (2012) [ZH Zhou] [234pp]
• Ensemble Approaches for Regression: A Survey (2012) [J Mendes-moreira, C Soares, AM Jorge] [40pp]

• A Survey on Metric Learning for Feature Vectors and Structured Data (2014) [A Bellet] [59pp]
• Metric Learning: A Survey (2012) [B Kulis] [80pp]

• Geometric Integrators and the Hamiltonian Monte Carlo Method (2017) [N Bou-rabee, JM Sanz-serna] [92pp]

• 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]
• A Tutorial on Thompson Sampling (2017) [D Russo, B VAN Roy, A Kazerouni, I Osband] [39pp]

• A Survey on Multi-View Learning (2013) [C Xu] [59pp]
• A Survey of Multi-View Machine Learning (2013) [S Sun] [13pp]

• 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] ⭐
• Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation (2017) [A Gatt, E Krahmer] [111pp] ⭐
• Opinion Mining and Sentiment Analysis (2012) [B Liu, L Zhang] [38pp]
• Neural Machine Translation and Sequence-to-Sequence Models: A Tutorial (2017) [G Neubig] [65pp]
• 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) [G Neubig] [57pp]

• Machine Learning & Artificial Intelligence in the Quantum Domain (2017) [V Dunjko, HJ Briegel] [106pp]

• 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] 📚

• 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 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]
• Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017) [J Janai, F Güney, A Behl, A Geiger] [14pp]
• Deep Learning for Video Game Playing (2017) [N Justesen, P Bontrager, J Togelius, S Risi] [16pp]

• 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 Literature Survey (2008) [X Zhu] [59pp]

• Learning With Submodular Functions: A Convex Optimization Perspective (2013) [F Bach] [173pp]
• Submodular Function Maximization (2012) [A Krause, D Golovin] [28pp]

• 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]

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