Let $z$ be a random variable representing an example for the learner (possibly an $(x,y)$ pair for supervised learning). Let $P(z)$ be the target training distribution from which the learner should ultimately learn a function of interest. Let $0 \leq W_{\lambda}(z) \leq 1$ be the weight applied to example $z$ at step $\lambda$ in the curriculum sequence, with $0 \leq \lambda \leq 1$, and $W_{1}(z) = 1$. The corresponding training distribution at step $\lambda$ is $Q_{\lambda}(z) \propto W_{\lambda}(z) P(z) \forall z$. Consider a monotonically increasing sequence of $\lambda$ values, starting from $\lambda = 0$ and ending at $\lambda = 1$. The corresponding sequence of distributions $Q_{\lambda}$ is a curriculum if the entropy of these distributions increases, i.e., $H(Q_{\lambda}) < H(Q_{\lambda} + \epsilon) \forall \epsilon > 0$, and $W_{\lambda}(z)$ is monotonically increasing in $\lambda$, i.e., $W_{\lambda+\epsilon}(z) \geq W_{\lambda}(z) \forall z, \forall \epsilon > 0$. This builds up a sequential training process where weights initially favor simpler examples that can be learned relatively easily. The training undergoes adaptation in weighting to increase the probability of difficult examples entering the training set, as a result of which the entropy increases.