An Autoencoder is a type of artificial neural network designed for unsupervised learning of efficient data representations. It consists of an Encoder that compresses input data into a lower-dimensional latent space and a Decoder that reconstructs the original data from this compressed form. Autoencoders are pivotal for tasks such as dimensionality reduction, denoising, and anomaly detection by capturing essential features and patterns within the data.