The nature of statistical learning theory / Vladimir N. Vapnik.

Learning theory : an approximation theory viewpoint
by: Cucker, Felipe, 1958-
Published: (2007)

Proceedings of the Twelfth Annual Conference on Computational Learning Theory : July 6-9, 1999, Santa Cruz, California
Published: (1999)

Proceedings of the eleventh annual ACM Conference on Computational Learning Theory : July 24th-26th, 1998 : Madison, Wisconsin
Published: (1998)

Introduction to deep learning
by: Charniak, Eugene
Published: (2018)

Proceedings of the Tenth Annual Conference on Computational Learning Theory : July 6th-9th, 1997, Nashville, Tennessee
Published: (1997)

Report of a workshop on the scope and nature of computational thinking
Published: (2010)

Statistical learning theory
by: Vapnik, Vladimir Naumovich
Published: (1998)

Machine Learning
by: Mitchell, Tom M. (Tom Michael), 1951-
Published: (1997)

Foundations of deep reinforcement learning : theory and practice in Python
by: Graesser, Laura, et al.
Published: (2020)

The principles of deep learning theory : an effective theory approach to understanding neural networks
by: Roberts, Daniel A., 1987-
Published: (2022)

The power of computational thinking : games, magic and puzzles to help you become a computational thinker
by: Curzon, Paul, et al.
Published: (2017)

The elements of statistical learning : data mining, inference, and prediction
by: Hastie, Trevor, et al.
Published: (2009)

The elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrations
by: Hastie, Trevor
Published: (2001)

Algorithmic aspects of machine learning
by: Moitra, Ankur, 1985-
Published: (2018)

Graphical models for machine learning and digital communication
by: Frey, Brendan J.
Published: (1998)

Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies
by: Kelleher, John D., 1974-
Published: (2015)

Handbook of evolutionary machine learning
Published: (2024)

Graphical models for machine learning and digital communication
by: Frey, Brendan J.
Published: (1998)

Machine learning for hackers
by: Conway, Drew
Published: (2012)

Machines that learn : based on the principles of empirical control
by: Brown, Robert Alan, 1938-
Published: (1994)

An introduction to computational learning theory
by: Kearns, Michael J.
Published: (1994)

Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory : July 27-29, 1992, Pittsburgh, Pennsylvania
Published: (1992)

The science of deep learning
by: Drori, Iddo
Published: (2023)

Data science and machine learning : mathematical and statistical methods
by: Kroese, Dirk P., et al.
Published: (2020)

Machine learning in production : developing and optimizing data science workflows and applications
by: Kelleher, Andrew, et al.
Published: (2019)

Deep learning
by: Goodfellow, Ian, et al.
Published: (2016)

Innovations in machine learning : theory and applications
Published: (2006)

Recurrent neural networks for prediction : learning algorithms, architectures and stability
by: Mandic, Danilo P.
Published: (2001)

Machine learning : discriminative and generative
by: Jebara, Tony
Published: (2004)

Modellbildung, Wissensrevision und Wissensrepräsentation im Maschinellen Lernen
by: Emde, Werner
Published: (1991)

A compendium of machine learning
by: Briscoe, Garry
Published: (1996)

Machine learning : neural networks, genetic algorithms, and fuzzy systems
by: Adeli, Hojjat, 1950-
Published: (1995)

Algorithmic learning
by: Hutchinson, Alan, 1948-
Published: (1994)

Introduction to machine learning
by: Alpaydin, Ethem
Published: (2020)

Machine learning refined : foundations, algorithms, and applications
by: Watt, Jeremy, et al.
Published: (2020)

Elements of machine learning
by: Langley, Pat
Published: (1996)