Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.
Text classification is a supervised learning task where the goal is to assign predefined categories to text data based on its content. It is widely used in applications like sentiment analysis, spam detection, and topic categorization, leveraging techniques from natural language processing and machine learning.
Pushdown Automata (PDA) are computational models that extend finite automata by including a stack, enabling them to recognize context-free languages. They are crucial for parsing nested structures, such as those found in programming languages and arithmetic expressions.
A Non-Deterministic Automaton (NDA) is a theoretical computational model where multiple outcomes are possible from any given state and input. Unlike deterministic automata, NDAs can have multiple transitions for the same input or even transitions without any input, allowing them to explore many computational paths simultaneously.