课程观看链接：https://www.bilibili.com/video/av66314465?p=1&rt=V%2FymTlOu4ow%2Fy4xxNWPUZwpjr6c6HRj9SG3qbZNakJg%3D 课程作业及更多资料的资源获取：https://blog.csdn.net/dadapongi6/article/details/105668394
以房价预测为例： 当给足够多的关于x,y的数据，即(x,y)训练样本，将会精确计算从x到y的精准映射函数。
Two definitions of Machine Learning are offered. Arthur Samuel described it as: “the field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition. Tom Mitchell provides a more modern definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” Example: playing checkers. E = the experience of playing many games of checkers T = the task of playing checkers. P = the probability that the program will win the next game. In general, any machine learning problem can be assigned to one of two broad classifications: Supervised learning and Unsupervised learning.
机器学习的两种定义：
在无需具体的编程规则的条件下，给予计算机以学习的能力计算机程序从经验E中学习某些类型的任务T和性能度量P，如果它在任务T中的性能(以P度量)随着经验E的提高而提高一些典型的神经网络 被应用于结构化数据和非结构化数据
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output. Supervised learning problems are categorized into “regression” and “classification” problems. In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories. Example 1: Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem. We could turn this example into a classification problem by instead making our output about whether the house “sells for more or less than the asking price.” Here we are classifying the houses based on price into two discrete categories. Example 2: (a) Regression - Given a picture of a person, we have to predict their age on the basis of the given picture (b) Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables. We can derive this structure by clustering the data based on relationships among the variables in the data. With unsupervised learning there is no feedback based on the prediction results. Example: Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on. Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).
印象非常深的话： Thanks to deep learning, thanks to neural networks, computers are now much better at interpeting unstructured data as well compared to just a few years ago. And this creates opportunities for many new exciting applications that use speech recognition, image recognition, natural language processing on text, much more than was possible even just two or three years ago.
Scale drives deep learning progress. 规模包括神经网络（一个有许多隐藏单元，许多参数，许多连接的神经网络）的规模，数据的规模。
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