Machine Learning in the Life Sciences
– An overview of global market outlook for machine learning in life sciences
– Identification of machine learning applications in life science sector
– Recent achievements in life science due to machine learning
– Strategies adopted in machine learning with focus on life science (e.g., types of algorithms, regression trees, artificial neural networks, and evolutionary computation)
– Information on Gaussian process models
– Coverage of major issues related to machine learning
– Analysis of current and emerging trends in machine learning
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Artificial intelligence (AI) is a term used to identify a scientific field that covers the creation of machines (e.g., robots) as well as computer hardware and software aimed at reproducing wholly or in part the intelligent behavior of human beings. AI is considered a branch of cognitive computing, a term that refers to systems able to learn, reason, and interact with humans. Cognitive computing is a combination of computer science and cognitive science.
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Artificial intelligence covers various aspects of human behavior including creativity, planning and scheduling, reasoning, imaging, writing, learning, auditing, and natural language processing. The concept of artificial intelligence, however, is in continuous evolution. In fact, once the use of machines with specific smart features becomes widespread, new systems with even more advanced capabilities are developed. By enhancing equipment functionality and productivity, AI is revolutionizing virtually every sector, from research and development to manufacturing and services.
Table of Contents
Chapter 1 Technology Highlights and Market Outlook
Artificial Intelligence and Machine Learning
Machine Learning Algorithms
General Applications of Machine Learning
Applications of Machine Learning in the Life Sciences
Current and Emerging Trends
Recent Achievements in the Life Sciences Due to Machine Learning
Market Outlook for Machine Learning in the Life Sciences
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