The course gives an introduction to machine learning techniques and theory, with a focus on its use in practical applications. During the course, a selection of topics will be covered in supervised learning, such as linear models for regression and classification, or nonlinear models such as neural networks, and in unsupervised learning such as clustering. The use cases and limitations of these algorithms will be discussed, and their implementation will be investigated in programming assignments. Methodological questions pertaining to the evaluation of machine learning systems will also be discussed, as well as some of the ethical questions that can arise when applying machine learning technologies. The use of machine learning components in practical applications will be exemplified, and realistic scenarios will be studied.
Feng, G., Qian, Z., Zhang, X. Evolutionary selection extreme learning machine optimization for regression (2012) Soft Computing, 16 (9), p.1485-1491. DOI: 10.1007/s00500-012-0823-7
Kumar, P., Singh, Y. A study on software reliability prediction models using soft computing techniques (2013) International Journal of Information and Communication Technology, 5 (2), p.187-204. DOI: 10.1504/IJICT.2013.053119
Mandal, I., Sairam, N. Enhanced classification performance using computational intelligence (2011) Communications in Computer and Information Science, 204 CCIS, p.384-391. DOI: 10.1007/978-3-642-24043-0_39 Other references will be shared with the students during the classes.
Silva, J.D.A., Hruschka, E.R. An experimental study on the use of nearest neighborbased imputation algorithms for classification tasks (2013) Data and Knowledge Engineering, 84, p.47-58. DOI: 10.1016/j.datak.2012.12.006