Machine Learning Theory

Machine Learning Theory is a branch of theoretical computer science that studies how computers can learn from data to make decisions or predictions. It focuses on understanding the algorithms and models that enable machines to recognize patterns and improve over time without being explicitly programmed. Key concepts include understanding the limits of what machines can learn, how they generalize knowledge from examples, and how to evaluate their performance. By analyzing these principles, researchers aim to create more efficient and effective learning systems that can adapt to new information.