Gaussian processes are a flexible and powerful framework for modeling complex, non-linear relationships in data. They are based on the concept of representing a distribution over functions, rather than specific point estimates. This allows for uncertainty quantification and propagation in predictions, making Gaussian processes particularly useful in tasks such as regression, classification, and optimization. In Gaussian processes, the data is assumed to be generated from a multivariate Gaussian distribution, with the covariance structure of the data capturing the relationships between observations. The key advantage of Gaussian processes is their ability to provide not only point estimates, but also full predictive distributions over possible outcomes. Gaussian processes have been successfully used in a wide range of applications, including time series forecasting, computer vision, natural language processing, and reinforcement learning. They offer a flexible and interpretable approach to modeling complex data, making them a popular choice for researchers and practitioners in machine learning and statistics.