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machine learning artificial intelligence pdf book collection

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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 An Introduction to Statistical Learning With Applications in Python [Robert Tibshirani,Jonathan Taylor] First Print July 2023.pdf Brendan J. Frey - Graphical Models for Machine Learning and Digital Communication (1998, The MIT Press) - libgen.li.pdf Carl Edward Rasmussen, Christopher K. I. Williams - Gaussian Processes for Machine Learning (2006, MIT Press).pdf Daphne Koller, Nir Friedman - Probabilistic Graphical Models_ Principles and Techniques (2009, The MIT Press).pdf David J. Hand, Heikki Mannila, Padhraic Smyth - Principles of data mining-MIT Press (2001).djvu Deep learning [Yoshua Bengio,Aaron Courville, Ian Goodfellow] - The MIT Press (2016) .pdf Elad Hazan - Introduction to Online Convex Optimization-The MIT Press (2022).epub Ethem Alpaydin - Introduction to Machine Learning (2020, The MIT Press) - libgen.li.pdf Freund, Yoav_Schapire, Robert E - Boosting foundations and algorithms-MIT Press (2012).pdf Gilbert Strang - Linear Algebra and Learning from Data (2019, Wellesley-Cambridge Press).pdf Jacob Eisenstein - Introduction to Natural Language Processing (Instructor's Solution Manual) (2019, The MIT Press).7z Jacob Eisenstein - Natural Language Processing-MIT Press(2018).pdf Jonas Peters, Dominik Janzing, Bernhard Schölkopf - Elements of Causal Inference_ Foundations and Learning Algorithms-The MIT Press (2017).pdf Lise Getoor, Ben Taskar - Introduction to Statistical Relational Learning (2007).pdf Machine Learning: A Probabilistic Perspective (Instructor's Solution Manual) [Kevin P. Murphy] - The MIT Press (2012).pdf Machine Learning: A Probabilistic Perspective [Kevin P. Murphy] - The MIT Press (2012).pdf Marc G. Bellemare - Distributional Reinforcement Learning - MIT Press (2023).epub 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 Masashi Sugiyama, Motoaki Kawanabe - Machine Learning in Non-Stationary Environments_ Introduction to Covariate Shift Adaptation (2012, The MIT Press).pdf Mehryar Mohri_ Afshin Rostamizadeh_ Ameet Talwalkar - Foundations of Machine Learning (2018, The MIT Press).pdf Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar - Foundations of Machine Learning, Second Edition [2nd Ed] (Instructor Res. last of 3, Figure.7z 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 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 Michael I. Jordan (Editor) - Learning in Graphical Models (Adaptive Computation and Machine Learning) (1998).pdf [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 Pattern Recognition and Machine Learning [Christopher Bishop] (2006).pdf Peter D. Grunwald, Jorma Rissanen - The minimum description length principle-MIT Press (2007).pdf Peter Spirtes, Clark Glymour, Richard Scheines - Causation, Prediction, and Search, Second Edition (2001, The MIT Press).pdf Pierre Baldi, Soren Brunak - Bioinformatics_ the machine learning approach-The MIT Press (2001).pdf Probabilistic Machine Learning: Advanced Topics [Kevin P. Murphy] - The MIT Press (2023).pdf Probabilistic Machine Learning: An Introduction [Kevin P. Murphy] (Instructor's Solution Manual) - The MIT Press (2022).pdf Probabilistic Machine Learning: An Introduction [Kevin P. Murphy] - The MIT Press (2022).pdf Ralf Herbrich - Learning Kernel Classifiers Theory and Algorithms (2001, The MIT Press).pdf Richard S. Sutton, Andrew G. Barto - Reinforcement learning_ an introduction (1998, The MIT Press).pdf [Springer Series in Statistics] Trevor Hastie, Robert Tibshirani, Jerome Friedman - The Elements of Statistical Learning_ Data Mining, Inference, and Prediction. (2013, Springer).pdf Stuart J. Russell, Peter Norvig - Artificial Intelligence_ A Modern Approach, Global Edition (2021, Pearson) - libgen.li.pdf 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 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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Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer, Francis Bach - Machine Learning for Data Streams_ with Practical Examples in MOA.pdf 20.9 MB
.pad/111887 109.3 KB
An Introduction to Statistical Learning With Applications in Python [Robert Tibshirani,Jonathan Taylor] First Print July 2023.pdf 19.2 MB
.pad/355244 346.9 KB
Brendan J. Frey - Graphical Models for Machine Learning and Digital Communication (1998, The MIT Press) - libgen.li.pdf 2.8 MB
.pad/231055 225.6 KB
Carl Edward Rasmussen, Christopher K. I. Williams - Gaussian Processes for Machine Learning (2006, MIT Press).pdf 2.7 MB
.pad/330311 322.6 KB
Daphne Koller, Nir Friedman - Probabilistic Graphical Models_ Principles and Techniques (2009, The MIT Press).pdf 8.4 MB
.pad/61159 59.7 KB
David J. Hand, Heikki Mannila, Padhraic Smyth - Principles of data mining-MIT Press (2001).djvu 4.6 MB
.pad/391027 381.9 KB
Deep learning [Yoshua Bengio,Aaron Courville, Ian Goodfellow] - The MIT Press (2016) .pdf 18.4 MB
.pad/114626 111.9 KB
Elad Hazan - Introduction to Online Convex Optimization-The MIT Press (2022).epub 14.5 MB
.pad/7200 7.0 KB
Ethem Alpaydin - Introduction to Machine Learning (2020, The MIT Press) - libgen.li.pdf 12.9 MB
.pad/100077 97.7 KB
Freund, Yoav_Schapire, Robert E - Boosting foundations and algorithms-MIT Press (2012).pdf 15.5 MB
.pad/486522 475.1 KB
Gilbert Strang - Linear Algebra and Learning from Data (2019, Wellesley-Cambridge Press).pdf 25.1 MB
.pad/467712 456.8 KB
Jacob Eisenstein - Introduction to Natural Language Processing (Instructor's Solution Manual) (2019, The MIT Press).7z 6.1 MB
.pad/454173 443.5 KB
Jacob Eisenstein - Natural Language Processing-MIT Press(2018).pdf 4.4 MB
.pad/128700 125.7 KB
Jonas Peters, Dominik Janzing, Bernhard Schölkopf - Elements of Causal Inference_ Foundations and Learning Algorithms-The MIT Press (2017).pdf 21.0 MB
.pad/37040 36.2 KB
Lise Getoor, Ben Taskar - Introduction to Statistical Relational Learning (2007).pdf 4.5 MB
.pad/504823 493.0 KB
Machine Learning: A Probabilistic Perspective (Instructor's Solution Manual) [Kevin P. Murphy] - The MIT Press (2012).pdf 1.7 MB
.pad/313881 306.5 KB
Machine Learning: A Probabilistic Perspective [Kevin P. Murphy] - The MIT Press (2012).pdf 25.7 MB
.pad/320307 312.8 KB
Marc G. Bellemare - Distributional Reinforcement Learning - MIT Press (2023).epub 13.4 MB
.pad/156588 152.9 KB
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 37.1 MB
.pad/471467 460.4 KB
Masashi Sugiyama, Motoaki Kawanabe - Machine Learning in Non-Stationary Environments_ Introduction to Covariate Shift Adaptation (2012, The MIT Press).pdf 12.1 MB
.pad/418917 409.1 KB
Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar - Foundations of Machine Learning, Second Edition [2nd Ed] (Instructor Res. last of 3, Figure.7z 1.7 MB
.pad/322694 315.1 KB
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 740.9 KB
.pad/289898 283.1 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 24.1 MB
.pad/457293 446.6 KB
Mehryar Mohri_ Afshin Rostamizadeh_ Ameet Talwalkar - Foundations of Machine Learning (2018, The MIT Press).pdf 8.3 MB
.pad/214484 209.5 KB
Michael I. Jordan (Editor) - Learning in Graphical Models (Adaptive Computation and Machine Learning) (1998).pdf 56.8 MB
.pad/173109 169.1 KB
Pattern Recognition and Machine Learning [Christopher Bishop] (2006).pdf 17.3 MB
.pad/259305 253.2 KB
Peter D. Grunwald, Jorma Rissanen - The minimum description length principle-MIT Press (2007).pdf 3.0 MB
.pad/508647 496.7 KB
Peter Spirtes, Clark Glymour, Richard Scheines - Causation, Prediction, and Search, Second Edition (2001, The MIT Press).pdf 3.1 MB
.pad/410859 401.2 KB
Pierre Baldi, Soren Brunak - Bioinformatics_ the machine learning approach-The MIT Press (2001).pdf 3.3 MB
.pad/222608 217.4 KB
Probabilistic Machine Learning: Advanced Topics [Kevin P. Murphy] - The MIT Press (2023).pdf 145.2 MB
.pad/300086 293.1 KB
Probabilistic Machine Learning: An Introduction [Kevin P. Murphy] (Instructor's Solution Manual) - The MIT Press (2022).pdf 614.7 KB
.pad/419167 409.3 KB
Probabilistic Machine Learning: An Introduction [Kevin P. Murphy] - The MIT Press (2022).pdf 80.3 MB
.pad/166693 162.8 KB
Ralf Herbrich - Learning Kernel Classifiers Theory and Algorithms (2001, The MIT Press).pdf 2.7 MB
.pad/324801 317.2 KB
Richard S. Sutton, Andrew G. Barto - Reinforcement learning_ an introduction (1998, The MIT Press).pdf 3.6 MB
.pad/430110 420.0 KB
Stuart J. Russell, Peter Norvig - Artificial Intelligence_ A Modern Approach, Global Edition (2021, Pearson) - libgen.li.pdf 32.5 MB
.pad/482628 471.3 KB
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 12.4 MB
.pad/79712 77.8 KB
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 30.5 MB
.pad/19770 19.3 KB
[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 6.3 MB
.pad/202714 198.0 KB
[Springer Series in Statistics] Trevor Hastie, Robert Tibshirani, Jerome Friedman - The Elements of Statistical Learning_ Data Mining, Inference, and Prediction. (2013, Springer).pdf 12.7 MB

Details for: Machine Learning Artificial Intelligence PDF book collection

machine learning artificial intelligence pdf book collection

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