On November 28, 2017, I gave a talk to the Machine Learning Research Group (MLRG) at the University of Guelph on different methods for teaching neural networks how to learn efficiently and effectively. Here, I reviewed a several methods in the literature such as: curriculum learning , variants of self-paced learning [2, 3], machine teaching  and meta-learning .
You can download my Google Slides in PDF.
More details coming soon…
 Bengio, Yoshua, Louradour, Jerome, Collobert, Ronan, and Weston, Jason. Curriculum learning. In Proceedings of the 26th International Conference on Machine Learning (ICML), pp. 41–48. ACM, 2009.
 Kumar, M Pawan, Packer, Benjamin, and Koller, Daphne. Self-paced learning for latent variable models. In Advances in Neural Information Processing Systems, pp. 1189–1197, 2010.
 Jiang, Lu, Meng, Deyu, Yu, Shoou-I, Lan, Zhenzhong, Shan, Shiguang, and Hauptmann, Alexander. Self-paced learning with diversity. In Advances in Neural Information Processing Systems (NIPS), pp. 2078–2086, 2014.
 Thrun, S. Lifelong Learning Algorithms, pp. 181–209. Springer US, Boston, MA, 1998. ISBN 978-1-4615- 5529-2.
 Zhu, X. Machine teaching: An inverse problem to machine learning and an approach toward optimal education. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pp. 4083–4087, 2015.