1.1.2 Machine Learning

 Machie learning can be divided into supervised learning, unsupervised learning, and reinforcement learning, as shown in Figure 1-2.

Supervised Learning. The supervised learning data set contains samples x and sample labels y. The algorithm needs to learn the mapping relationship f0: x->y, where f0 represents the model function and  are the parameters of the model. During training, the model parameters  are optimized by  minimizing errors between the model prediction and the real value y, so that the model can have more accurate prediction. Common supervised learning models include linear regression, logistic regression, support vector machines (SVMs), and random forests.

Unsupervised Learning. Collecting labeled data is often more expensive. For a sample-only data set, the algorithm needs to discover the modalities of the data itself. This kind of algorithm is called unsupervised learning. One type of algorithm in unsupervised learning uses itself as a supervised signal, that is, f:x->x, which is known as self-supervised learning. During training, parameters are optimized by minimizing the error between the model's predicted value f(x) and itself x, common unsupervised learning algorithms include self-encoders and generative adversarial networks(GANs)

Reinforcement Learning. This is a type of algorithm that learns strategies for solving problems by  interacting with the environment, Unlike supervised and unsupervised learning, reinforcement learning problems do not have a clear "correct" action supervision signal. The algorithm needs to interact with the environment to obtain a lagging reward signal from the environmental feedback. Therefore, it is not possible to calculate the errors between model Reinforcement Learning prediction and "correct values" to optimize the network directly. Common reinforcement learning algorithms are Deep Q-Networks(DQNs) and Proximal Policy Optimizaiton(PPO).