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In machine learning, formal grammar refers to a set of rules that define the syntactic structure of a language. These rules specify how valid sentences or sequences of symbols can be formed within that language. Formal grammars are used in natural language processing (NLP) and other areas of machine learning to model the structure and syntax of human languages. 

There are different types of formal grammars, and one commonly used type is the context-free grammar (CFG). CFGs consist of a set of production rules that define how different elements of a language can be combined. These grammars are often used in parsing algorithms to analyze and understand the structure of sentences in natural language. 

In machine learning, formal grammars can be employed for tasks such as syntactic analysis, parsing, and generation of language. For example, in the development of chatbots or language understanding models, formal grammars can help define the expected structure of user inputs and guide the model in generating appropriate responses. 

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