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Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer, Francis Bach - Machine Learning for Data Streams_ with Practical Examples in MOA.pdf
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20.89 MB
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An Introduction to Statistical Learning With Applications in Python [Robert Tibshirani,Jonathan Taylor] First Print July 2023.pdf
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19.16 MB
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Brendan J. Frey - Graphical Models for Machine Learning and Digital Communication (1998, The MIT Press) - libgen.li.pdf
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2.78 MB
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Carl Edward Rasmussen, Christopher K. I. Williams - Gaussian Processes for Machine Learning (2006, MIT Press).pdf
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2.68 MB
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Daphne Koller, Nir Friedman - Probabilistic Graphical Models_ Principles and Techniques (2009, The MIT Press).pdf
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8.44 MB
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David J. Hand, Heikki Mannila, Padhraic Smyth - Principles of data mining-MIT Press (2001).djvu
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DJVU
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4.63 MB
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Deep learning [Yoshua Bengio,Aaron Courville, Ian Goodfellow] - The MIT Press (2016) .pdf
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18.39 MB
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Elad Hazan - Introduction to Online Convex Optimization-The MIT Press (2022).epub
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14.49 MB
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Ethem Alpaydin - Introduction to Machine Learning (2020, The MIT Press) - libgen.li.pdf
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PDF
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12.9 MB
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Freund, Yoav_Schapire, Robert E - Boosting foundations and algorithms-MIT Press (2012).pdf
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15.54 MB
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Gilbert Strang - Linear Algebra and Learning from Data (2019, Wellesley-Cambridge Press).pdf
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25.05 MB
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Jacob Eisenstein - Introduction to Natural Language Processing (Instructor's Solution Manual) (2019, The MIT Press).7z
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7Z
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6.07 MB
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Jacob Eisenstein - Natural Language Processing-MIT Press(2018).pdf
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4.38 MB
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Jonas Peters, Dominik Janzing, Bernhard Schölkopf - Elements of Causal Inference_ Foundations and Learning Algorithms-The MIT Press (2017).pdf
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20.96 MB
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Lise Getoor, Ben Taskar - Introduction to Statistical Relational Learning (2007).pdf
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4.52 MB
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Machine Learning: A Probabilistic Perspective (Instructor's Solution Manual) [Kevin P. Murphy] - The MIT Press (2012).pdf
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1.7 MB
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Machine Learning: A Probabilistic Perspective [Kevin P. Murphy] - The MIT Press (2012).pdf
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25.69 MB
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Marc G. Bellemare - Distributional Reinforcement Learning - MIT Press (2023).epub
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EPUB
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13.35 MB
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Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai - Machine Learning from Weak Supervision_ An Empirical Risk Minimization Approach (2022, The MIT Press) - li.pdf
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37.05 MB
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Masashi Sugiyama, Motoaki Kawanabe - Machine Learning in Non-Stationary Environments_ Introduction to Covariate Shift Adaptation (2012, The MIT Press).pdf
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12.1 MB
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Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar - Foundations of Machine Learning, Second Edition [2nd Ed] (Instructor Res. last of 3, Figure.7z
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7Z
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1.69 MB
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Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar - Foundations of Machine Learning, Second Edition [2nd Ed] (Instructor Res. n. 1 of 3, Solution Manual, Solutions) (2018.pdf
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740.9 KB
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Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar - Foundations of Machine Learning, Second Edition [2nd Ed] (Instructor Res. n. 2 of 3, Lectures) (2018, The MIT Press) - .7z
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7Z
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24.06 MB
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Mehryar Mohri_ Afshin Rostamizadeh_ Ameet Talwalkar - Foundations of Machine Learning (2018, The MIT Press).pdf
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8.3 MB
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Michael I. Jordan (Editor) - Learning in Graphical Models (Adaptive Computation and Machine Learning) (1998).pdf
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56.83 MB
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Pattern Recognition and Machine Learning [Christopher Bishop] (2006).pdf
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17.25 MB
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Peter D. Grunwald, Jorma Rissanen - The minimum description length principle-MIT Press (2007).pdf
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3.01 MB
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Peter Spirtes, Clark Glymour, Richard Scheines - Causation, Prediction, and Search, Second Edition (2001, The MIT Press).pdf
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3.11 MB
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Pierre Baldi, Soren Brunak - Bioinformatics_ the machine learning approach-The MIT Press (2001).pdf
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3.29 MB
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Probabilistic Machine Learning: Advanced Topics [Kevin P. Murphy] - The MIT Press (2023).pdf
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145.21 MB
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Probabilistic Machine Learning: An Introduction [Kevin P. Murphy] (Instructor's Solution Manual) - The MIT Press (2022).pdf
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614.66 KB
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Probabilistic Machine Learning: An Introduction [Kevin P. Murphy] - The MIT Press (2022).pdf
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80.34 MB
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Ralf Herbrich - Learning Kernel Classifiers Theory and Algorithms (2001, The MIT Press).pdf
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2.69 MB
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Richard S. Sutton, Andrew G. Barto - Reinforcement learning_ an introduction (1998, The MIT Press).pdf
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3.59 MB
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Stuart J. Russell, Peter Norvig - Artificial Intelligence_ A Modern Approach, Global Edition (2021, Pearson) - libgen.li.pdf
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32.54 MB
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Stuart Russell, Peter Norvig - Artificial Intelligence_ A Modern Approach, Fourth Global Edition [4th Ed] (Instructor Res. n. 1 of 2, Solution Manual, Solutions)-Pearson Education Limited (2021).7z
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7Z
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12.42 MB
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Stuart Russell, Peter Norvig - Artificial Intelligence_ A Modern Approach, Fourth Global Edition [4th Ed] (Instructor Res. n. last of 2, Lectures) (2021, Pearson Education Limited) - libgen.li.7z
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7Z
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30.48 MB
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[Morgan Kaufmann Series in Data Management Systems] Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal - Data Mining_ Practical Machine Learning Tools and Techniques (2016, Morgan Kaufmann Publishers).pdf
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6.31 MB
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[Springer Series in Statistics] Trevor Hastie, Robert Tibshirani, Jerome Friedman - The Elements of Statistical Learning_ Data Mining, Inference, and Prediction. (2013, Springer).pdf
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12.69 MB
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