Machine Learning for Civil and Environmental Engineers, Naser
Автор: Hector Zenil, Jesper Tegner, Narsis A. Kiani Название: Algorithmic Information Dynamics: A Computational Approach to Causality with Applications to Living Systems ISBN: 1108497667 ISBN-13(EAN): 9781108497664 Издательство: Cambridge Academ Рейтинг: Цена: 63350.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Biological systems are extensively studied as interactions forming complex networks. Reconstructing causal knowledge from, and principles of, these networks from noisy and incomplete data is a challenge in the field of systems biology. Based on an online course hosted by the Santa Fe Institute Complexity Explorer, this book introduces the field of Algorithmic Information Dynamics, a model-driven approach to the study and manipulation of dynamical systems . It draws tools from network and systems biology as well as information theory, complexity science and dynamical systems to study natural and artificial phenomena in software space. It consists of a theoretical and methodological framework to guide an exploration and generate computable candidate models able to explain complex phenomena in particular adaptable adaptive systems, making the book valuable for graduate students and researchers in a wide number of fields in science from physics to cell biology to cognitive sciences.
Автор: Xiaohong Chen; Norman R. Swanson Название: Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis ISBN: 148999971X ISBN-13(EAN): 9781489999719 Издательство: Springer Рейтинг: Цена: 172350.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Written by leading experts in the field, this volume presents cutting-edge results on specification and estimation of economic models. Advances in asymptotic approximation theory are discussed, as well as the use of various theoretical tools for technique development.
Автор: Nandi Anirban, Pal Aditya Kumar Название: Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods ISBN: 1484278011 ISBN-13(EAN): 9781484278017 Издательство: Неизвестно Рейтинг: Цена: 55170.00 T Наличие на складе: Невозможна поставка. Описание: Chapter 1: Introduction to Machine Learning DomainChapter Goal: The book's opening chapter will talk about the journey of machine learning models and why model interpretability became so important in the recent times. This chapter will also cover some of the basic black box modelling algorithms in brief Sub-Topics: - Journey of machine learning domain- Journey of machine learning algorithms - Why only reporting accuracy is not enough for models Chapter 2: Introduction to Model InterpretabilityChapter Goal: This chapter will talk about the importance and need of interpretability and how businesses employ model interpretability for their decisionsSub-Topics: - Why is interpretability needed for machine learning models- Motivation behind using model interpretability- Understand social and commercial motivations for machine learning interpretability, fairness, accountability, and transparency- Get a definition of interpretability and learn about the groups leading interpretability research Chapter 3: Machine Learning Interpretability TaxonomyChapter Goal: A machine learning taxonomy is presented in this section. This will be used to characterize the interpretability of various popular machine learning techniques.Sub topics: - Understanding and trust- A scale for interpretability- Global and local interpretability- Model-agnostic and model-specific interpretability Chapter 4: Common Properties of Explanations Generated by Interpretability MethodsChapter goal: The purpose of this chapter to explain readers about evaluation metrics for various interpretability methods. This will help readers understand which methods to choose for specific use cases Sub topics: - Degree of importance - Stability- Consistency - Certainty- Novelty Chapter 5: Timeline of Model interpretability Methods DiscoveryChapter goal: This chapter will talk about the timeline and will give details about when most common methods of interpretability were discovered Chapter 6: Unified Framework for Model ExplanationsChapter goal: Each method is determined by three choices: how it handles features, what model behavior it analyzes, and how it summarizes feature influence. The chapter will focus in detail about each step and will try to map different methods to each step by giving detailed examplesSub topics1: - Removal based explanations- Summarization based explanations Chapter 7: Different Types of Removal Based ExplanationsChapter goal: This chapter will talk about the different types of removal based methods and how to implement them along with details of examples and Python packages, real life use cases etc.Sub topics: - IME(2009)- IME(2010)- QII- SHAP- KernelSHAP- TreeSHAP- LossSHAP- SAGE- Shapley- Shapley- Permutation- Conditional- Feature- Univariate- L2X- INVASE- LIME- LIME- PredDiff- Occlusion- CXPlain- RISE- MM- MIR- MP- EP- FIDO-CA Chapter 8: Different Types of Summarization Based ExplanationsChapter goal: This chapter will talk about the different types of summarization based methods and how to implement them along with details of examples and python p
Автор: Sшvik Atle Ottesen Название: Free Will, Causality and the Self ISBN: 3110611740 ISBN-13(EAN): 9783110611748 Издательство: Walter de Gruyter Рейтинг: Цена: 27580.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
A major goal for compatibilists is to avoid the luck problem and to include all the facts from neuroscience and natural science in general which purportedly show that the brain works in a law-governed and causal way like any other part of nature. Libertarians, for their part, want to avoid the manipulation argument and demonstrate that very common and deep seated convictions about freedom and responsibility are true: it can really be fundamentally up to us as agents to determine that the future should be either A or B.
This book presents a theory of free will which integrates the main motivations of compatibilists and libertarians, while at the same time avoiding their problems. The so-called event-causal libertarianism is the libertarian account closest to compatibilitsm, as it claims there is indeterminism in the mind of an agent. The charge of compatibilists, however, is that this position is impaired by the problem of luck. This book is unique in arguing that free will in a strong sense of the term does not require indeterminism in the brain, only indeterminism somewhere in the world which there plausibly is.
In the past fifty years statisticians and methodologists in the social sciences have developed and refined a family of closely related statistical methods for the study of social phenomena. While the value of such methods of analysis is universally acknowledged, their use has never been wholly uncontroversial. In 1993 prominent scholars from a variety of disciplines (social sciences, statistics, philosophy of science) gathered at the University of Notre Dame to debate whether causal modeling techniques old or new can really justify the drawing of causal conclusions on the basis of correlational statistical data. The resulting volume from that groundbreaking conference is Causality in Crisis? a comprehensive and sophisticated introduction to perhaps the most important set of issues confronting social scientific researchers in the 1990s and beyond.
In the essays presented here contributors critically reassess the widely accepted view that statistical methods of analysis can and do yield causal understanding of social phenomena. Although a number of technical issues receive attention, the overall emphasis is on the larger historical, philosophical, and conceptual perspectives that underlie and inform current methodological controversies.
The debates in Causality in Crisis? have far-ranging implications, for on their resolution hinges the question of what sort of knowledge of social life it is possible to achieve on the basis of non-experimental social scientific research. Any scholar who makes use of causal methods, as well as all who are affected by decisions reached on the basis of such methods, will have a stake in the challenging arguments put forth in this volume.
Автор: Mark J. van der Laan; James M Robins Название: Unified Methods for Censored Longitudinal Data and Causality ISBN: 1441930558 ISBN-13(EAN): 9781441930552 Издательство: Springer Рейтинг: Цена: 153720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A fundamental statistical framework for the analysis of complex longitudinal data is provided in this book. The techniques go beyond standard statistical approaches and can be used to teach masters and Ph.D. The text is ideally suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data.
Автор: Samantha Kleinberg Название: Time and Causality Across the Sciences ISBN: 1108476678 ISBN-13(EAN): 9781108476676 Издательство: Cambridge Academ Рейтинг: Цена: 61240.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides an entry point for researchers in any field, bringing together perspectives collected from a large body of work on causality across disciplines. Topics include whether quantum mechanics allows causes to precede their effects, the integration of mechanisms, and insight into the role played by intervention and timing information.
Автор: Poli Roberto Название: Causality and Motivation ISBN: 3110329395 ISBN-13(EAN): 9783110329391 Издательство: Walter de Gruyter Рейтинг: Цена: 219850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The series provides a forum for innovative, high-quality work in all fields of analytical philosophy. The volumes in this series are published in either English or German.
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