Machine learning is about computational methods that enable machines to learn concepts and improve performance from experience. Here, experience can take diverse forms, including data examples, abstract knowledge, interactions and feedback from the environment, other models, and so forth. Depending on different assumptions on the types and amount of experience available there are different learning paradigms, such as supervised learning, active learning, reinforcement learning, knowledge distillation, adversarial learning, and combinations thereof. On the other hand, a hallmark of human intelligence is the ability to learn from all sources of information. In this workshop, we aim to explore various aspects of learning paradigms, particularly theoretical properties and formal connections between them, and new algorithms combining multiple modes of supervisions, etc.