BUiD Library: INF 513 Machine Learning: Reading list

INF 513 Machine Learning

Module Description: Machine learning is about making computers learn, rather than simply programming them to do tasks. The course will discuss supervised learning (which is concerned with learning to predict an output, from given inputs), reinforcement learning (which is concerned about learning from interacting with an environment), unsupervised learning, where we wish to discover the structure in a set of patterns; there is no output "teacher signal". We will compare and contrast different learning algorithms, and unlike Data Mining Exploration module where the focus was on the applying algorithms to large real-world data sets, in this course we will get to the technical and mathematical details of the studied algorithms.

Module text(s)

The lecture notes are designed to be self-contained, with pointers to web-resources and related material. Recommended readings include

Crandall, J. W., Oudah, M., Ishowo-Oloko, F., Abdallah, S., Bonnefon, J., Cebrian, M., Shariff, A., Goodrich, M.A. and Rahwan, I. (2018). Cooperating with machines. Nature communications, vol. 9(1), pp. 233.
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J. & Liu, H. (2017). Feature selection: a data perspective. ACM Computing Surveys (CSUR), vol. 50(6), pp. 94.
Abdallah, S. & Kaisers, M. (2016). Addressing environment non-stationarity by repeating Q-learning updates. The Journal of Machine Learning Research, vol. 17(1), pp. 1582-1612.
Gu, B., Sheng, V. S., Tay, K. Y., Romano, W. & Li, S. (2015). Incremental support vector learning for ordinal regression. IEEE Transactions on Neural networks and learning systems, vol. 26(7), pp. 1403-1416. Request item
Li, J., Hu, X., Jian, L. & Liu, H. (2016). Toward time-evolving feature selection on dynamic networks. In Data Mining (ICDM), 2016 IEEE 16th International Conference on (pp. 1003-1008). IEEE. Request item
MacKay, D. (2003). Information theory, inference, and learning algorithms. Cambridge: Cambridge University Press. Open resource

Chapters in the following books are interesting to read