In machine learniong weak learners are sub-models that are in themselves not that good, but can potentially be combined, or modified in someway to make them stringer. For example, random forest techniques create large numbers of {[decision trees}} each of which may not be that effective, but when combined by higher-level decision algorithm (such as weighted voting), have proven very powerful.
Used on Chap. 16: page 387