Two intelligent abilities and three inverse problems are re-elaborated from a probability theory based two pathway perspective, with challenges of statistical learning and efforts towards the challenges overviewed. Then, a detailed introduction is provided on the Bayesian Ying-Yang (BYY) harmony learning. Proposed firstly in (Xu,1995) and systematically developed in the past decade, this approach consists of a two pathway featured BYY system as a general framework for unifying a number of typical learning models, and a best Ying-Yang harmony principle as a general theory for parameter learning and model selection. The BYY harmony learning leads to not only a criterion that outperforms typical model selection criteria in a two-phase implementation, but also model selection made automatically during parameter learning for several typical learning tasks, with computing cost saved significantly. In addition to introducing the fundamentals, several typical learning approaches are also systematically compared and re-elaborated from the BYY harmony learning perspective. Moreover, a further brief is made on the features and applications of a particular family called Gaussian manifold based BYY systems.