Machine Learning is the fundamentals of Artificial Intelligence. Understanding the concepts, theory, process and mechanism in Machine Learning is the key to understand all the areas in Artificial Intelligence.
Deep Learning uses a cascade of multiple layers of nonlinear processing units, called Neural Networks, for feature extraction and transformation. Deep Learning is the foundation for most of the AI technology.
Computer Vision is a field of AI that deals with how computers can gain high-level understanding of the visual world from images or videos. It seeks to automate tasks that the human visual system can do.
Deep Reinforcement Learning
Deep Reinforcement Learning is an area of AI that deals with how agents take actions in an environment so as to maximize reward. In real word, DRL is about the optimal control of an autonomous system.
Unsupervised learning is an area of AI that deals with finding unknown patterns in a data set without labels and with a minimum of human supervision, in contrast to supervised learning that uses labeled data.
Self-supervised learning opens up a huge opportunity for better utilizing unlabelled data, while learning in a supervised learning manner with pretext tasks designed for learning representation.
Natural Language Processing
Natural language processing is an area of AI that deals with the interactions between computers and humans using natural language, in particular how to train computers to understand large amounts of natural language data.
Automated Machine Learning (AutoML) provides methods and processes to make Machine Learning available for non AI experts, to improve efficiency of Machine Learning and to accelerate research on ML.