Machine learning artificial intelligence books collection | 34A485F6415E9B32200ADABC7EF8FF84BB493FA5

Machine learning artificial intelligence books collection

Name	machine learning artificial intelligence books collection	DOWNLOAD Copy Link
Total Size	761.5 MB
Total Files	77
Last Seen	2024-10-06 23:43
Hash	34A485F6415E9B32200ADABC7EF8FF84BB493FA5

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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	21.9 MB
An Introduction to Statistical Learning With Applications in Python [Robert Tibshirani,Jonathan Taylor] First Print July 2023.pdf	20.1 MB
Brendan J. Frey - Graphical Models for Machine Learning and Digital Communication (1998, The MIT Press) - libgen.li.pdf	2.9 MB
Carl Edward Rasmussen, Christopher K. I. Williams - Gaussian Processes for Machine Learning (2006, MIT Press).pdf	2.8 MB
Daphne Koller, Nir Friedman - Probabilistic Graphical Models_ Principles and Techniques (2009, The MIT Press).pdf	8.9 MB
David J. Hand, Heikki Mannila, Padhraic Smyth - Principles of data mining-MIT Press (2001).djvu	4.9 MB
Deep learning [Yoshua Bengio,Aaron Courville, Ian Goodfellow] - The MIT Press (2016) .pdf	19.3 MB
Elad Hazan - Introduction to Online Convex Optimization-The MIT Press (2022).epub	15.2 MB
Ethem Alpaydin - Introduction to Machine Learning (2020, The MIT Press) - libgen.li.pdf	13.5 MB
Freund, Yoav_Schapire, Robert E - Boosting foundations and algorithms-MIT Press (2012).pdf	16.3 MB
Gilbert Strang - Linear Algebra and Learning from Data (2019, Wellesley-Cambridge Press).pdf	26.3 MB
Jacob Eisenstein - Introduction to Natural Language Processing (Instructor's Solution Manual) (2019, The MIT Press).7z	6.4 MB
Jacob Eisenstein - Natural Language Processing-MIT Press(2018).pdf	4.6 MB
Jonas Peters, Dominik Janzing, Bernhard Schölkopf - Elements of Causal Inference_ Foundations and Learning Algorithms-The MIT Press (2017).pdf	22.0 MB
Lise Getoor, Ben Taskar - Introduction to Statistical Relational Learning (2007).pdf	4.7 MB
Machine Learning: A Probabilistic Perspective (Instructor's Solution Manual) [Kevin P. Murphy] - The MIT Press (2012).pdf	1.8 MB
Machine Learning: A Probabilistic Perspective [Kevin P. Murphy] - The MIT Press (2012).pdf	26.9 MB
Marc G. Bellemare - Distributional Reinforcement Learning - MIT Press (2023).epub	14.0 MB
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	38.9 MB
Masashi Sugiyama, Motoaki Kawanabe - Machine Learning in Non-Stationary Environments_ Introduction to Covariate Shift Adaptation (2012, The MIT Press).pdf	12.7 MB
Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar - Foundations of Machine Learning, Second Edition [2nd Ed] (Instructor Res. last of 3, Figure.7z	1.8 MB
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	758.7 KB
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	25.2 MB
Mehryar Mohri_ Afshin Rostamizadeh_ Ameet Talwalkar - Foundations of Machine Learning (2018, The MIT Press).pdf	8.7 MB
Michael I. Jordan (Editor) - Learning in Graphical Models (Adaptive Computation and Machine Learning) (1998).pdf	59.6 MB
Pattern Recognition and Machine Learning [Christopher Bishop] (2006).pdf	18.1 MB
Peter D. Grunwald, Jorma Rissanen - The minimum description length principle-MIT Press (2007).pdf	3.2 MB
Peter Spirtes, Clark Glymour, Richard Scheines - Causation, Prediction, and Search, Second Edition (2001, The MIT Press).pdf	3.3 MB
Pierre Baldi, Soren Brunak - Bioinformatics_ the machine learning approach-The MIT Press (2001).pdf	3.4 MB
Probabilistic Machine Learning: Advanced Topics [Kevin P. Murphy] - The MIT Press (2023).pdf	152.3 MB
Probabilistic Machine Learning: An Introduction [Kevin P. Murphy] (Instructor's Solution Manual) - The MIT Press (2022).pdf	629.4 KB
Probabilistic Machine Learning: An Introduction [Kevin P. Murphy] - The MIT Press (2022).pdf	84.2 MB
Ralf Herbrich - Learning Kernel Classifiers Theory and Algorithms (2001, The MIT Press).pdf	2.8 MB
Richard S. Sutton, Andrew G. Barto - Reinforcement learning_ an introduction (1998, The MIT Press).pdf	3.8 MB
Stuart J. Russell, Peter Norvig - Artificial Intelligence_ A Modern Approach, Global Edition (2021, Pearson) - libgen.li.pdf	34.1 MB
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	13.0 MB
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	32.0 MB
[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	6.6 MB
[Springer Series in Statistics] Trevor Hastie, Robert Tibshirani, Jerome Friedman - The Elements of Statistical Learning_ Data Mining, Inference, and Prediction. (2013, Springer).pdf	13.3 MB
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Total files 77