Machine Learning and Its Applications

"Machine Learning and Its Applications" systematically introduces the basic theory and application technology of machine learning. First, the basic knowledge necessary to master machine learning theories and methods is introduced, including the basic concepts and development history of machine learning, and basic methods of model construction and optimization; then, traditional machine learning theories and methods such as supervised learning, unsupervised learning, ensemble learning, and reinforcement learning are introduced and discussed; on the basis of a detailed discussion of the basic theories of neural networks and deep learning, the basic theories and training paradigms of several typical deep learning models such as deep convolutional networks, deep recurrent networks, and generative adversarial networks are introduced, and the basic theories and methods of deep reinforcement learning are analyzed and discussed. "Machine Learning and Its Applications" is written from the perspective of senior undergraduates and junior master's students. It uses simple language to accurately express knowledge content in simple and easy-to-understand terms, and focuses on highlighting the ideological connotation and essence of machine learning methods, so that readers can grasp the main content of the book. Each chapter of "Machine Learning and Its Applications" is equipped with a certain number of exercises. It is suitable as an entry-level machine learning textbook for undergraduates or graduate students in intelligent science and technology, data science and big data technology, and computer-related majors. It can also be used as a learning reference for engineering technicians and self-study readers.