We provide an algorithm for properly learning mixtures of two single-dimensional gaussians without any separability assumptions. Given $\tilde{O}(1/\varepsilon^2)$ samples from an unknown mixture, our algorithm outputs a mixture that is $\varepsilon$-close in →