Lecture 10: Instance-Based vs. Model-Based Learning in AI

This lecture delves into the intricate world of machine learning by contrasting two fundamental approaches: Instance-Based and Model-Based Learning. We begin by exploring Instance-Based Learning (IBL), which includes algorithms like k-Nearest Neighbors (k-NN), where predictions are made by closely examining specific instances from the training dataset without deriving an explicit model. The session will highlight the advantages of IBL, such as its simplicity and adaptability to new data, while also discussing its limitations in terms of scalability and efficiency with large datasets.

Transitioning to Model-Based Learning, we will investigate how these algorithms, including decision trees, neural networks, and support vector machines, construct an explicit model to make predictions. This section will cover the strengths of Model-Based Learning, such as its ability to generalize from training data and its effectiveness in handling complex patterns, alongside potential challenges like overfitting and the need for extensive computational resources.

Throughout the lecture, we will engage in a comparative analysis of these approaches through various lenses, including accuracy, interpretability, computational efficiency, and applicability to different types of problems in AI. By the end of this session, attendees will gain a comprehensive understanding of these methodologies, empowering them to make informed decisions about which approach to leverage for specific machine learning challenges.