What are the key principles of machine learning relevant to PSE Cortex?

The key principles of machine learning that are most relevant to PSE Cortex include supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning involves training a model on a labeled dataset, where the input data is paired with the correct output. This approach is crucial for tasks such as classification and regression, enabling the model to make predictions based on new, unseen data after it learns from the training examples.

Unsupervised learning, on the other hand, deals with unlabeled data and focuses on uncovering hidden patterns or intrinsic structures within the data. Techniques such as clustering fall under this category, making it valuable when there is no prior information about the data classes.

Reinforcement learning is a distinct area where an agent learns to make decisions by taking actions in an environment to maximize a cumulative reward. This approach is applicable in various applications, such as robotics and game playing, reflecting its versatility in different scenarios.

While other options contain concepts relevant to machine learning and data analysis, they do not encompass the broad spectrum of learning frameworks that directly align with the foundational principles in machine learning, making option B the most comprehensive and applicable choice in this context.