We strive to derive human-like machine intelligence from interactive and autonomous learning experiences. We focus on exploiting/uncovering the merits of Bayesian learning that provides a principled way of allowing machine intelligence to adapt and generalize to unseen data/environments in conjunction with deep learning models and frameworks. We focus on building and promoting interactive systems within which humans and machines (AI agents) collaborate, cooperate, and coordinate through verbal and non-verbal communicative signals. The multi-modal nature of such interactive systems leads our research trajectory to revolve around the research areas including generative modeling, natural language processing (NLP), data mining, human-computer interaction (HCI), and optimization.