  Conventional machine learning (ML) relies heavily on manual design from
machine learning experts to decide learning tasks, data, models, optimization
algorithms, and evaluation metrics, which is labor-intensive, time-consuming,
and cannot learn autonomously like humans. In education science, self-directed
learning, where human learners select learning tasks and materials on their own
without requiring hands-on guidance, has been shown to be more effective than
passive teacher-guided learning. Inspired by the concept of self-directed human
learning, we introduce the principal concept of Self-directed Machine Learning
(SDML) and propose a framework for SDML. Specifically, we design SDML as a
self-directed learning process guided by self-awareness, including internal
awareness and external awareness. Our proposed SDML process benefits from self
task selection, self data selection, self model selection, self optimization
strategy selection and self evaluation metric selection through self-awareness
without human guidance. Meanwhile, the learning performance of the SDML process
serves as feedback to further improve self-awareness. We propose a mathematical
formulation for SDML based on multi-level optimization. Furthermore, we present
case studies together with potential applications of SDML, followed by
discussing future research directions. We expect that SDML could enable
machines to conduct human-like self-directed learning and provide a new
perspective towards artificial general intelligence.
