concept vs classification learning
symbolic vs statistical learning
inductive vs analytical learning
all above
Answer is Right!
Answer is Wrong!
The correct answer is D. all above.
Machine learning is a field of computer science that gives computer systems the ability to “learn” (i.e., progressively improve performance on a task) with data, without being explicitly programmed.
There are many different approaches to machine learning, but some of the most common include:
- Concept learning is a type of machine learning in which the goal is to learn a concept from a set of examples. For example, a concept learning algorithm might be used to learn the concept of “dog” from a set of images of dogs.
- Classification learning is a type of machine learning in which the goal is to learn a function that can assign labels to new data. For example, a classification learning algorithm might be used to learn a function that can predict whether a new image is a dog or a cat.
- Symbolic learning is a type of machine learning in which the goal is to learn a symbolic representation of a concept. For example, a symbolic learning algorithm might be used to learn a rule that states that “all dogs have four legs.”
- Statistical learning is a type of machine learning in which the goal is to learn a statistical model that can be used to make predictions about new data. For example, a statistical learning algorithm might be used to learn a model that can predict the price of a house based on its features.
- Inductive learning is a type of machine learning in which the goal is to learn a general rule from a set of specific examples. For example, an inductive learning algorithm might be used to learn the rule “all dogs have four legs” from a set of examples of dogs.
- Analytical learning is a type of machine learning in which the goal is to learn a model that can be used to explain the data. For example, an analytical learning algorithm might be used to learn a model that can explain why some dogs have long hair and others have short hair.
These are just a few of the many different approaches to machine learning. The best approach to use depends on the specific problem that is being solved.