What type of machine learning is characterized by using multiple layers of information to make complex decisions?

Deep learning is a subset of machine learning that utilizes neural networks with multiple layers, known as deep neural networks, to process and learn from vast amounts of data. This multi-layer architecture enables the model to automatically learn intricate patterns and representations in the data, which are crucial for making complex decisions.

The depth of these networks allows for hierarchical feature extraction, where the first layers might learn simple patterns, and as information progresses through the layers, the network captures more abstract concepts. For example, in image recognition tasks, early layers might detect edges, while deeper layers might identify shapes, objects, or even more intricate features.

The other approaches mentioned, such as shallow learning or reinforcement learning, do not share the same characteristics. Shallow learning typically involves simpler models with fewer layers, limiting their ability to discern complex patterns. Reinforcement learning focuses on learning policies based on feedback from actions taken in an environment rather than extracting feature representations from layers of data in a structured manner. Transductive learning deals with the use of labeled data to make predictions about unlabeled data but does not necessarily involve deep, layered architectures for decision-making.