Artificial Intelligence and Marketing
Routledge (Verlag)
9781138606180 (ISBN)
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The evolution of AI and ML, and the background to their use in marketing
The objectives and goals
The approaches and tools that can be utilised
The philosophical and ethical questions raised
With a unique combination of theory and practice, including numerous practical examples, this book is particularly suitable for advanced undergraduate and postgraduate students and academics with an interest in marketing research, strategic marketing management, Big Data and technology, and innovation. It will also be of interest to any marketing practitioners looking for a thorough grounding in the theory and applications.
James Seligman is a retired Principal Fellow of the Marketing Subject Group at the Business School at Southampton University, UK, with over 30 years of multinational brand experience. A fellow of the Chartered Institute Marketing, his research interests lie in management and marketing with a focus on insight, analytics, communications, brand and behaviour, and technology.
About the Author, Preface, Objectives, SECTION ONE: Artificial Intelligence, Chapter 1 Overview, Chapter 2 Goals, 2.1 Reasoning, problem solving, 2.2 Knowledge representation, 2.3 Planning, 2.4 Learning, 2.5 Natural language processing, 2.6 Perception, 2.7 Motion and manipulation, 2.8 Social intelligence, 2.9 Creativity, 2.10 General intelligence, Chapter 3 Approaches, 3.1 Cybernetics and brain simulation, 3.2 Symbolic, 3.3 Sub-symbolic, 3.4 Statistical, 3.5 Integrating the approaches, Chapter 4 Tools, 4.1 Search and optimization, 4.2 Logic, 4.3 Probabilistic methods for uncertain reasoning, 4.4 Classifiers and statistical learning methods, 4.5 Neural networks, 4.6 Deep feedforward neural networks, 4.7 Deep recurrent neural networks, 4.8 Control theory, 4.9 Languages, 4.10 Evaluating progress, Chapter 5 Applications, 5.1 Competitions and prizes, 5.2 Healthcare, 5.3 Automotive, 5.4 Finance, 5.5 Video games, Chapter 6 Platforms, 6.1 Partnership on AI, Chapter 7 Philosophy and ethics, 7.1 The limits of artificial general intelligence, 7.2 Potential risks and moral reasoning, 7.3 Machine consciousness, sentience and mind, 7.4 Superintelligence, References, SECTION TWO: MACHINE LEARNING, Chapter 8 Overview, 8.1 Types of problems and tasks, Chapter 9 History and relationships to other fields, 9.1 Relation to statistics, Chapter 10 Theory, Chapter 11 Approaches, 11.1 Decision tree learning, 11.2 Association rule learning, 11.3 Artificial neural networks, 11.4 Deep learning, 11.5 Inductive logic programming, 11.6 Support vector machines, 11.7 Clustering, 11.8 Bayesian networks, 11.9 Reinforcement learning, 11.10 Representation learning, 11.11 Similarity and metric learning, 11.12 Sparse dictionary learning, 11.13 Genetic algorithms, 11.14 Rule-based machine learning, Chapter 12 Applications, Chapter 13 Model assessments, Chapter 14 Ethics, Chapter 15 Software, 15.1 Free and open-source software, 15.2 Proprietary software with free and open-source editions, 15.3 Proprietary software, 15.4 Machine Learning Future in Marketing, References, Bibliography and Reading, Index.
| Erscheint lt. Verlag | 31.1.2019 |
|---|---|
| Zusatzinfo | 18 Line drawings, black and white; 18 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Wirtschaft ► Betriebswirtschaft / Management ► Marketing / Vertrieb | |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| ISBN-13 | 9781138606180 / 9781138606180 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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