机器学习是理论性和应用性并重的学科，发展机器学习理论和方法的最终目的还

Machine learning is a subject that emphasizes both theory and application. The ultimate goal of developing machine learning theories and methods is to serve the needs of actual production and life. The development of machine learning itself is to a large extent based on problems from reality. , The solution method breaks through in theory, and finally is tested and revised in practice. Therefore, the theme of "learning" runs through the teaching of this course. For example, when a class starts, it is first to clarify what is learning, why the machine should be used to learn, and what to learn. After having preliminary answers to these questions, you are faced with the questions of how to turn human knowledge into machine knowledge, what difficulties are often encountered in the learning process, and what are the ways to overcome them. After the machine acquires a certain ability through learning, how to apply it to practical problems. This naturally leads to supervised learning, unsupervised learning, parameterization methods and so on in subsequent chapters. Try to guide students to abandon the idealized conceptual model of the undergraduate stage, look at the problem from the perspective of realization, guide students to think about how to turn human knowledge into machine knowledge through a certain model, and help students from passive acceptance learning at the undergraduate stage to the graduate stage Qualitative leap in research-based learning

Machine learning is a theoretical and applied discipline, the ultimate goal of developing machine learning theory and methods is to serve the needs of actual production and life, the development of machine learning discipline itself is also to a large extent along the problem from the actual, the solution from the theoretical breakthrough, and finally in practice to test and revise the development of ideas. Therefore, the theme of "learning" is always running through the teaching of this course. Such as the beginning of the course to clarify what is learning, why let the machine to learn, learn what and so on. After the initial answer to these questions, we are faced with the problemof how to turn human knowledge into machine knowledge, what difficulties will be encountered in the learning process, and what ways to overcome them. How the machine acquires a certain ability by learning to apply to practical problems. This naturally leads to the follow-up chapters of supervised learning, unsupervised learning, parametric methods and so on. Try to guide students to abandon the idealized conceptual model of undergraduate stage, look at the problem from the perspective of realization, guide students to think about how to turn human knowledge into machine knowledge through a certain model, and help students to cross from passive acceptance of undergraduate learning to the leap of research-based learning quality in the postgraduate stage.

Machine learning is a discipline that emphasizes both theory and application. The ultimate goal of developing machine learning theory and method is to serve the needs of actual production and life. To a great extent, the development of machine learning discipline is also a development idea of coming from reality, solving methods from theory, and finally testing and correcting in practice. Therefore, in the teaching of this course, the theme of "learning" always runs through. For example, first of all, we should make clear what learning is, why we should let the machine learn and what to learn. With the preliminary answers to these questions, we are faced with such problems as how to turn human knowledge into machine knowledge, what difficulties we often encounter in the learning process, and what ways to overcome them. After learning to acquire certain ability, how to apply it to practical problems. This naturally leads to the following chapters of supervised learning, unsupervised learning, parametric methods and so on. Try to guide students to discard the idealized conceptual model of undergraduate stage, look at the problem from the perspective of realization, guide students to think about how to turn human knowledge into machine knowledge through a certain model, and help students leap from passive acceptance learning in undergraduate stage to research-based learning in graduate stage<br>