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.
- 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 Literature Survey (2010) [B Settles] [67pp]
- Introduction to Bioinformatics (2013) [A Lesk] [255pp] 📚
- Bioinformatics - an Introduction for Computer Scientists (2004) [J Cohen] [37pp]
- 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]
- 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: 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]
- 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 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] ⭐
- 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) [G Neubig] [57pp]
- 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]
- 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] ⭐