Livros sobre o tema "Probability learning"
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Batanero, Carmen, Egan J. Chernoff, Joachim Engel, Hollylynne S. Lee e Ernesto Sánchez. Research on Teaching and Learning Probability. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31625-3.
DasGupta, Anirban. Probability for Statistics and Machine Learning. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9634-3.
Peck, Roxy. Statistics: Learning from data. Australia: Brooks/Cole, Cengage Learning, 2014.
Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18545-9.
Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30717-6.
Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04648-3.
Powell, Warren B. Optimal learning. Hoboken, New Jersey: Wiley, 2012.
Vapnik, Vladimir Naumovich. The Nature of Statistical Learning Theory. New York, NY: Springer New York, 1995.
DasGupta, Anirban. Probability for statistics and machine learning: Fundamentals and advanced topics. New York: Springer, 2011.
Wan, Shibiao. Machine learning for protein subcellular localization prediction. Boston: De Gruyter, 2015.
Velleman, Paul F. Learning data analysis with Data desk. New York: W.H. Freeman, 1993.
Velleman, Paul F. Learning data analysis with Data desk. New York: W.H. Freeman, 1989.
Lim, Chee Peng. An incremental adaptive network for on-line, supervised learning and probability estimation. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.
Gabbay, Dov M. Abductive Reasoning and Learning. Dordrecht: Springer Netherlands, 2000.
Summa, Mireille Gettler. Statistical learning and data science. Boca Raton: CRC Press, 2012.
Palfrey, Thomas R. Testing game-theoretic models of free riding: New evidence on probability bias and learning. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1990.
Rasmussen, Carl Edward. Gaussian processes for machine learning. Cambridge, Mass: MIT Press, 2006.
Rasmussen, Carl Edward. Gaussian processes for machine learning. Cambridge, MA: MIT Press, 2005.
Vidyasagar, M. Learning and Generalisation: With Applications to Neural Networks. London: Springer London, 2003.
Dehmer, Matthias. Statistical and machine learning approaches for network analysis. Hoboken, N.J: Wiley, 2012.
Berk, Richard. Criminal Justice Forecasts of Risk: A Machine Learning Approach. New York, NY: Springer New York, 2012.
Thathachar, Mandayam A. L. Networks of learning automata: Techniques for online stochastic optimization. Boston: Kluwer Academic, 2004.
Thathachar, Mandayam A. L. Networks of learning automata: Techniques for online stochastic optimization. Boston, MA: Kluwer Academic, 2003.
Everitt, Brian. The analysis of contingency tables. 2a ed. London: Chapman & Hall, 1992.
Koltchinskii, Vladimir. Oracle inequalities in empirical risk minimization and sparse recovery problems: École d'été de probabilités de Saint-Flour XXXVIII-2008. Berlin: Springer Verlag, 2011.
Baram, Yoram. Estimation and classification by sigmoids based on mutual information. [Washington, D.C: National Aeronautics and Space Administration, 1994.
Dietrich, Albert, ed. Knowledge structures. Berlin: Springer-Verlag, 1994.
Sanner, Scott. Recent Advances in Reinforcement Learning: 9th European Workshop, EWRL 2011, Athens, Greece, September 9-11, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Flach, Peter A. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Flach, Peter A. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Peck, Roxy, e Chris Olsen. Statistics: Learning from Data. Brooks/Cole, 2013.
Peck, Roxy, e Tom Short. Statistics: Learning from Data. Brooks/Cole, 2017.
Unpingco, José. Python for Probability, Statistics, and Machine Learning. Springer, 2019.
Unpingco, José. Python for Probability, Statistics, and Machine Learning. Springer International Publishing AG, 2022.
Unpingco, José. Python for Probability, Statistics, and Machine Learning. Springer, 2016.
Unpingco, José. Python for Probability, Statistics, and Machine Learning. Springer London, Limited, 2016.
Unpingco, José. Python for Probability, Statistics, and Machine Learning. Springer, 2020.
Peck, Roxy. Statistics: Learning from Data. Cengage Learning, 2023.
Peck, Roxy. Statistics: Learning from Data. Brooks/Cole, 2013.
The Art of Statistics: Learning from Data. Pelican Books, 2019.
The Art of Statistics: Learning from Data. Great Britain: Pelican Books, 2019.
Research Institute for Advanced Computer Science (U.S.), ed. Bayesian learning. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.
Knox, Steven W. Machine Learning: a Concise Introduction (Wiley Series in Probability and Statistics). Wiley, 2018.
Jones, Graham A. Exploring Probability in School: Challenges for Teaching and Learning. Springer, 2010.
Batanero, Carmen, e Egan J. Chernoff. Teaching and Learning Stochastics: Advances in Probability Education Research. Springer, 2018.
Jones, Graham A. Exploring Probability in School: Challenges for Teaching and Learning. Springer, 2005.
Batanero, Carmen, e Egan J. Chernoff. Teaching and Learning Stochastics: Advances in Probability Education Research. Springer, 2019.
Duerr, Oliver, e Beate Sick. Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability. Manning Publications Co. LLC, 2020.
ERIC Clearinghouse for Science, Mathematics, and Environmental Education., ed. Resources for teaching and learning about probability and statistics. [Columbus, Ohio]: ERIC Clearinghouse for Science, Mathematics and Environmental Education, 1999.
Duerr, Oliver, Beate Sick e Elvis Murina. Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability. Manning Publications, 2020.