Artificial Intelligence Artificial Intelligence

Artificial Intelligence

A Textbook

    • $44.99
    • $44.99

Publisher Description

This textbook covers the broader field of artificial intelligence.   The chapters for this textbook span within three categories:
Deductive reasoning methods: These methods start with pre-defined hypotheses and reason with them in order to arrive at logically sound conclusions. The underlying methods include search and logic-based methods. These methods are discussed in Chapters 1through 5.Inductive Learning Methods:  These methods start with examples and use statistical methods in order to arrive at hypotheses. Examples include regression modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models. These methods are discussed in Chapters~6 through 11. Integrating Reasoning and Learning:  Chapters~11 and 12 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence.
The primary audience for this textbook are professors and advanced-level students in computer science. It is also possible to use this textbook for the mathematics requirements for an undergraduate data science course. Professionals working in this related field many also find this textbook useful as a reference.

GENRE
Computers & Internet
RELEASED
2021
July 16
LANGUAGE
EN
English
LENGTH
503
Pages
PUBLISHER
Springer International Publishing
SELLER
Springer Nature B.V.
SIZE
29.2
MB

More Books Like This

Introduction to Artificial Intelligence Introduction to Artificial Intelligence
2018
Advanced Artificial Intelligence Advanced Artificial Intelligence
2019
Inductive Logic Programming Inductive Logic Programming
2008
Intelligent Systems Intelligent Systems
2011
Advances in Artificial Intelligence Advances in Artificial Intelligence
2007
KI 2007: Advances in Artificial Intelligence KI 2007: Advances in Artificial Intelligence
2007

More Books by Charu C. Aggarwal

Neural Networks and Deep Learning Neural Networks and Deep Learning
2018
Linear Algebra and Optimization for Machine Learning Linear Algebra and Optimization for Machine Learning
2020
Recommender Systems Recommender Systems
2016
Data Mining Data Mining
2015
Social Network Data Analytics Social Network Data Analytics
2011
Machine Learning for Text Machine Learning for Text
2018