Question

Unsupervised machine learning involves:

A. using labeled inputs identified by humans to recognize objects.
B. programs that can detect digital photos of human faces and cats without humans labeling the input photos.
C. using algorithms to simulate the neurons and synapses of human brains.
D. using very large databases to store common sense knowledge, then searching the database for patterns.
E. using genetic algorithms to identify patters in large datasets.

Answer 1

Unsupervised machine learning is option B, where programs can identify patterns in data without needing labeled input photos. It is a type of artificial intelligence that does not use labeled datasets but rather detects structures within the data.

Step-by-step explanation:

Unsupervised machine learning involves B. programs that can detect digital photos of human faces and cats without humans labeling the input photos. This form of machine learning does not rely on labeled datasets; instead, it works by identifying patterns and structures within the data itself. For example, unsupervised learning algorithms may cluster digital images based on pixel similarities without any prior knowledge of what the images contain.

Its applications are vast, ranging from the categorization of galaxies in projects like Galaxy Zoo to the detection of anomalies in complex datasets. The process of unsupervised learning aligns with the broader scope of artificial intelligence, which includes various subfields such as genetic algorithms and neural networks, both aimed at simulating or leaning on forms of human cognition and decision-making strategies.