Complete Machine Learning Notes for BCA Final Year Students
BCADS-517 MACHINE LEARNING UNIT I: (8 Sessions) Introduction: Learning theory, Hypothesis, and target class, Inductive bias and bias-variance trade-off, Occam's razor, Limitations of inference machines, Approximation and estimation errors for skill development and employability. 1. Learning Theory Learning theory in Machine Learning (ML) is a framework that helps us understand how algorithms can learn patterns and make predictions from data. It provides a theoretical foundation for understanding the capabilities and limitations of various machine learning algorithms. Learning theory explores questions like: 1. Generalization: How well does a model perform on new, unseen data? Can it generalize the patterns it learned from the training data to make accurate predictions on new instances? 2. Overfitting and Underfitting: When is a model too complex (overfitting)