In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is
presented as varying along a dimension. The collection of dimensions then form the …

The current processes for building machine learning systems require practitioners with deep
knowledge of machine learning. This significantly limits the number of machine learning …

Although there are many online resources for youngsters to start learning about machine
learning on their own, a majority of them require solid mathematical or programming …

We study the empirical strategies that humans follow as they teach a target concept with a
simple 1D threshold to a robot. Previous studies of computational teaching, particularly the …

In this paper, we consider the problem of machine teaching, the inverse problem of machine
learning. Different from traditional machine teaching which views the learners as batch …

The concept of mastery learning is powerful: rather than a fixed number of practices,
students continue to practice a skill until they have mastered it. However, an implicit …

Given its societal impacts and applications to numerous fields, machine learning (ML) is an
important topic to understand for many students outside of computer science and statistics …

We propose Teacher-Student Curriculum Learning (TSCL), a framework for automatic
curriculum learning, where the Student tries to learn a complex task, and the Teacher …

In this paper, we make an important step towards the black-box machine teaching by
considering the cross-space machine teaching, where the teacher and the learner use …

Over the past decades, numerous practical applications of machine learning techniques
have shown the potential of AI-driven and data-driven approaches in a large number of …