Basics of Machine Learning

In the past decades, MLMachine learning has achieved significant advances in both theory and applications. Generally speaking, MLMachine learning has three major categories: supervised learning, unsupervised learning, and reinforcement learning based on the data characteristics. Supervised learning trains a model that can learn and infer the mapping function between the input data and their corresponding labels. Compared with the other two categories, supervised learning is the most active branch in MLMachine learning research and is widely used in many current MLMachine learning applications.