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Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
AI Adoption.
Classic textbook on AI, covering a wide range of topics from the history of AI to the latest advances in machine learning. It is written by two of the leading researchers in the field, and it is considered one of the best textbooks on AI available.
Provides a comprehensive overview of the benefits and challenges of AI adoption, covering topics such as the impact of AI on business, the ethical implications of AI, and the future of AI. It is written by two leading experts in the field, and it is considered one of the best books on AI adoption available.
Practical guide to machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It is written by one of the leading researchers in the field, and it is considered one of the best books on machine learning available.
Comprehensive guide to reinforcement learning, covering topics such as Markov decision processes, value functions, and policy gradients. It is written by two of the leading researchers in the field, and it is considered one of the best books on reinforcement learning available.
Practical guide to natural language processing, covering topics such as tokenization, stemming, lemmatization, and parsing. It is written by three of the leading researchers in the field, and it is considered one of the best books on natural language processing available.
Comprehensive guide to computer vision, covering topics such as image formation, feature detection, and object recognition. It is written by one of the leading researchers in the field, and it is considered one of the best books on computer vision available.
Comprehensive guide to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It is written by three of the leading researchers in the field, and it is considered one of the best books on deep learning available.
Provides a clear and concise overview of AI, covering topics such as machine learning, natural language processing, and computer vision. It is written by one of the leading researchers in the field, and it is considered one of the best books on AI available for beginners.
Provides a clear and concise overview of machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It is written in a clear and concise style, making it accessible to readers with no prior knowledge of machine learning.
Provides a clear and concise overview of reinforcement learning, covering topics such as Markov decision processes, value functions, and policy gradients. It is written in a clear and concise style, making it accessible to readers with no prior knowledge of reinforcement learning.
Provides a clear and concise overview of natural language processing, covering topics such as tokenization, stemming, lemmatization, and parsing. It is written in a clear and concise style, making it accessible to readers with no prior knowledge of natural language processing.
Provides a clear and concise overview of computer vision, covering topics such as image formation, feature detection, and object recognition. It is written in a clear and concise style, making it accessible to readers with no prior knowledge of computer vision.
Provides a comprehensive overview of AI, covering topics such as machine learning, natural language processing, and computer vision. It is written in a clear and concise style, making it accessible to readers with no prior knowledge of AI.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/431rsd/ai
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