Mark Girolami – författare

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20 produkter

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20 produkter

First Course in Machine Learning
AvSimon Rogers,Mark Girolami
Häftad, Engelska, 2020
681 kr
Skickas inom 10-15 vardagar
"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."—Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."—Daniel Barbara, George Mason University, Fairfax, Virginia, USA"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."—Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength…Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."—David Clifton, University of Oxford, UK"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." —Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."—Guangzhi Qu, Oakland University, Rochester, Michigan, USA
Handbook of Statistical Systems Biology
AvMichael Stumpf,David J. Baldingm. fl.
Inbunden, Engelska, 2011
2 248 kr
Skickas inom 11-20 vardagar
Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book: Provides a comprehensive account of inference techniques in systems biology.Introduces classical and Bayesian statistical methods for complex systems.Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems.Discusses various applications for statistical systems biology, such as gene regulation and signal transduction.Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies.Presents an in-depth presentation of reverse engineering approaches.Provides colour illustrations to explain key concepts.This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.
Handbook of Statistical Systems Biology
AvMark Girolami,David J. Baldingm. fl.
E-bok
Engelska, 2011
2 616 kr
Läs direkt efter köp
Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters.
This book:
Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts.
This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.
Handbook of Statistical Systems Biology
AvMark Girolami,David J. Baldingm. fl.
E-bok
PDF, Engelska, 2011
2 643 kr
Läs direkt efter köp
Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters.
This book:
Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts.
This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.
Advances in Independent Component Analysis
AvMark Girolami
E-bok
PDF, Engelska, 2012
2 543 kr
Läs direkt efter köp
Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.
Self-Organising Neural Networks
Independent Component Analysis and Blind Source Separation
AvMark Girolami
E-bok
PDF, Engelska, 2012
1 455 kr
Läs direkt efter köp
The conception of fresh ideas and the development of new techniques for Blind Source Separation and Independent Component Analysis have been rapid in recent years. It is also encouraging, from the perspective of the many scientists involved in this fascinating area of research, to witness the growing list of successful applications of these methods to a diverse range of practical everyday problems. This growth has been due, in part, to the number of promising young and enthusiastic researchers who have committed their efforts to expanding the current body of knowledge within this field of research. The author of this book is among one of their number. I trust that the present book by Dr. Mark Girolami will provide a rapid and effective means of communicating some of these new ideas to a wide international audience and that in turn this will expand further the growth of knowledge. In my opinion this book makes an important contribution to the theory of Independent Component Analysis and Blind Source Separation. This opens a range of exciting methods, techniques and algorithms for applied researchers and practitioner engineers, especially from the perspective of artificial neural networks and information theory. It has been interesting to see how rapidly the scientific literature in this area has grown.
First Course in Machine Learning
AvSimon Rogers,Mark Girolami
Inbunden, Engelska, 2016
1 077 kr
Skickas inom 10-15 vardagar
"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."—Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."—Daniel Barbara, George Mason University, Fairfax, Virginia, USA"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."—Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength…Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."—David Clifton, University of Oxford, UK"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." —Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."—Guangzhi Qu, Oakland University, Rochester, Michigan, USA
First Course in Machine Learning
AvMark Girolami,Simon Rogers
E-bok
PDF, Engelska, 2016
771 kr
Läs direkt efter köp
"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."—Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden
"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."—Daniel Barbara, George Mason University, Fairfax, Virginia, USA
"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."—Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark
"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength…Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."—David Clifton, University of Oxford, UK
"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." —Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK
"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."—Guangzhi Qu, Oakland University, Rochester, Michigan, USA
First Course in Machine Learning
AvMark Girolami,Simon Rogers
E-bok
Engelska, 2016
771 kr
Läs direkt efter köp
"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."—Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden
"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."—Daniel Barbara, George Mason University, Fairfax, Virginia, USA
"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."—Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark
"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength…Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."—David Clifton, University of Oxford, UK
"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." —Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK
"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."—Guangzhi Qu, Oakland University, Rochester, Michigan, USA
Self-Organising Neural Networks
Independent Component Analysis and Blind Source Separation
AvMark Girolami
Häftad, Engelska, 1999
1 142 kr
Skickas inom 10-15 vardagar
The conception of fresh ideas and the development of new techniques for Blind Source Separation and Independent Component Analysis have been rapid in recent years. It is also encouraging, from the perspective of the many scientists involved in this fascinating area of research, to witness the growing list of successful applications of these methods to a diverse range of practical everyday problems. This growth has been due, in part, to the number of promising young and enthusiastic researchers who have committed their efforts to expanding the current body of knowledge within this field of research. The author of this book is among one of their number. I trust that the present book by Dr. Mark Girolami will provide a rapid and effective means of communicating some of these new ideas to a wide international audience and that in turn this will expand further the growth of knowledge. In my opinion this book makes an important contribution to the theory of Independent Component Analysis and Blind Source Separation. This opens a range of exciting methods, techniques and algorithms for applied researchers and practitioner engineers, especially from the perspective of artificial neural networks and information theory. It has been interesting to see how rapidly the scientific literature in this area has grown.
Advances in Independent Component Analysis
AvMark Girolami
Häftad, Engelska, 2000
2 049 kr
Skickas inom 10-15 vardagar
Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.
Advances in Information Retrieval
24th BCS-IRSG European Colloquium on IR Research Glasgow, UK, March 25-27, 2002 Proceedings
AvFabio Crestani,Mark Girolamim. fl.
Häftad, Engelska, 2002
574 kr
Skickas inom 10-15 vardagar
The annual colloquium on information retrieval research provides an opportunity for both new and established researchers to present papers describing work in progress or ?nal results. This colloquium was established by the BCS IRSG(B- tish Computer Society Information Retrieval Specialist Group), and named the Annual Colloquium on Information Retrieval Research. Recently, the location of the colloquium has alternated between the United Kingdom and continental Europe. To re?ect the growing European orientation of the event, the colloquium was renamed “European Annual Colloquium on Information Retrieval Research” from 2001. Since the inception of the colloquium in 1979 the event has been hosted in the city of Glasgow on four separate occasions. However, this was the ?rst time that the organization of the colloquium had been jointly undertaken by three separate computer and information science departments; an indication of the collaborative nature and diversity of IR research within the universities of the West of Scotland. The organizers of ECIR 2002 saw a sharp increase in the number of go- quality submissions in answer to the call for papers over previous years and as such 52 submitted papers were each allocated 3 members of the program committee for double blind review of the manuscripts. A total of 23 papers were eventually selected for oral presentation at the colloquium in Glasgow which gave an acceptance rate of less than 45% and ensured a very high standard of the papers presented.
Advances in Information Retrieval
24th BCS-IRSG European Colloquium on IR Research Glasgow, UK, March 25-27, 2002 Proceedings
AvC.J.van Rijsbergen,Mark Girolamim. fl.
E-bok
PDF, Engelska, 2003
739 kr
Läs direkt efter köp
The annual colloquium on information retrieval research provides an opportunity for both new and established researchers to present papers describing work in progress or ?nal results. This colloquium was established by the BCS IRSG(B- tish Computer Society Information Retrieval Specialist Group), and named the Annual Colloquium on Information Retrieval Research. Recently, the location of the colloquium has alternated between the United Kingdom and continental Europe. To re?ect the growing European orientation of the event, the colloquium was renamed “European Annual Colloquium on Information Retrieval Research” from 2001. Since the inception of the colloquium in 1979 the event has been hosted in the city of Glasgow on four separate occasions. However, this was the ?rst time that the organization of the colloquium had been jointly undertaken by three separate computer and information science departments; an indication of the collaborative nature and diversity of IR research within the universities of the West of Scotland. The organizers of ECIR 2002 saw a sharp increase in the number of go- quality submissions in answer to the call for papers over previous years and as such 52 submitted papers were each allocated 3 members of the program committee for double blind review of the manuscripts. A total of 23 papers were eventually selected for oral presentation at the colloquium in Glasgow which gave an acceptance rate of less than 45% and ensured a very high standard of the papers presented.
Pattern Recognition in Bioinformatics
4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009, Proceedings
AvVisakan Kadirkamanathan,Guido Sanguinettim. fl.
Häftad, Engelska, 2009
558 kr
Skickas inom 10-15 vardagar
The Pattern Recognition in Bioinformatics (PRIB) meeting was established in 2006 under the auspices of the International Association for Pattern Recognition (IAPR) to create a focus for the development and application of pattern recognition techniques in the biological domain. PRIB's aim to explore the full spectrum of pattern recognition application was re?ected in the breadth of techniquesrepresented in this year's subm- sions and in this book. These range from image analysis for biomedical data to systems biology. We werefortunatetohaveinvitedspeakersofthehighestcalibredeliveringkeynotes at the conference. These were Pierre Baldi (UC Irvine), Alvis Brazma (EMBL-EBI), GunnarRats .. ch(MPITubi .. ngen)andMichaelUnser(EPFL).Weacknowledgesupportof theEUFP7NetworkofExcellencePASCAL2forpartiallyfundingtheinvitedspeakers. Immediately prior to the conference, we hosted half day of tutorial lectures, while a special session on "Machine Learningfor IntegrativeGenomics" was held immediately after the main conference.Duringthe conference,a poster session was heldwith further discussion.Wewouldlikeonceagaintothankalltheauthorsforthehighqualityofsubmissions, as well as Yorkshire South and the University of Shef?eld for providing logistical help in organizing the conference. Finally, we would like to thank Springer for their help in assembling this proceedings volume and for the continued support of PRIB.
Pattern Recognition in Bioinformatics
4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009, Proceedings
AvJosselin Noirel,Mahesan Niranjanm. fl.
E-bok
PDF, Engelska, 2009
710 kr
Läs direkt efter köp
Artificial Neural Networks and Machine Learning - ICANN 2011
21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I
AvTimo Honkela,Włodzisław Duchm. fl.
Häftad, Engelska, 2011
574 kr
Skickas inom 10-15 vardagar
This two volume set LNCS 6791 and LNCS 6792 constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.
Artificial Neural Networks and Machine Learning - ICANN 2011
21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I
AvSamuel Kaski,Mark Girolamim. fl.
E-bok
PDF, Engelska, 2011
739 kr
Läs direkt efter köp
This two volume set LNCS 6791 and LNCS 6792 constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.
Artificial Neural Networks and Machine Learning - ICANN 2011
21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II
AvTimo Honkela,Włodzisław Duchm. fl.
Häftad, Engelska, 2011
574 kr
Skickas inom 10-15 vardagar
This two volume set (LNCS 6791 and LNCS 6792) constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.
Artificial Neural Networks and Machine Learning - ICANN 2011
21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II
AvSamuel Kaski,Mark Girolamim. fl.
E-bok
PDF, Engelska, 2011
732 kr
Läs direkt efter köp
This two volume set (LNCS 6791 and LNCS 6792) constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.
Learning and Inference in Computational Systems Biology
AvNeil D. Lawrence,Mark Girolamim. fl.
Inbunden, Engelska, 2010
187 kr
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Mark Girolami – författare

First Course in Machine Learning

Handbook of Statistical Systems Biology

Handbook of Statistical Systems Biology

Handbook of Statistical Systems Biology

Advances in Independent Component Analysis

Self-Organising Neural Networks

First Course in Machine Learning

First Course in Machine Learning

First Course in Machine Learning

Self-Organising Neural Networks

Advances in Independent Component Analysis

Advances in Information Retrieval

Advances in Information Retrieval

Pattern Recognition in Bioinformatics

Pattern Recognition in Bioinformatics

Artificial Neural Networks and Machine Learning - ICANN 2011

Artificial Neural Networks and Machine Learning - ICANN 2011

Artificial Neural Networks and Machine Learning - ICANN 2011

Artificial Neural Networks and Machine Learning - ICANN 2011

Learning and Inference in Computational Systems Biology

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