Which algorithm should the ML team use to meet this requirement?

Explanations:

Principal Component Analysis (PCA) is a dimensionality reduction technique, primarily used for feature extraction and visualization, rather than for classification tasks. It does not classify images but helps reduce their complexity.

Recurrent Neural Networks (RNNs) are designed for sequential data processing, such as time series or natural language processing, rather than for image data. They are not well-suited for image classification tasks like detecting logos in images.

K-nearest neighbors (k-NN) is a simple classification algorithm that can work with image data, but it is generally not effective for high-dimensional data like images due to its reliance on distance metrics. It does not scale well and is typically outperformed by more advanced algorithms, especially for complex tasks such as image classification.

Convolutional Neural Networks (CNNs) are specifically designed for image processing and classification tasks. They use convolutional layers to automatically learn spatial hierarchies of features, making them highly effective for detecting patterns in images, such as logos.