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LUBS2095
Module Reading List

Understanding Data in the Social Sciences, 2021/22, Semester 2
Danat Valizade
d.valizade@leeds.ac.uk
Tutor information is taken from the Module Catalogue

Principles of machine learning

Breiman, L., 2001. Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), pp.199-231.

Bzdok, D., N. Altman and M. Krzywinski, M., 2018. Statistics versus machine learning, Nature methods15(4), p.233.

Easterby-Smith, M., Jaspersen, L.J., Thorpe, R. and Valizade, D., 2021. Management and business research (Chapter 11, pp. 387-413). Sage.

McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J. and Barton, D., 2012. Big data: the management revolution. Harvard business review90(10), pp.60-68.

 

Time series forecasting

ARIMA using Python - https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/ 

Chen, P., Yuan, H. and Shu, X., 2008, October. Forecasting crime using the arima model. In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (Vol. 5, pp. 627-630). IEEE.

Sezer, O.B., Gudelek, M.U. and Ozbayoglu, A.M., 2020. Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing90, p.106181.

 

Ensemble methods

Breiman, L., 2001. Random forests. Machine learning, 45(1), pp.5-32.

 

Interpretable machine learning

Molnar, C., 2020. Interpretable Machine Learning https://christophm.github.io/interpretable-ml-book/

 

Natural language processing

Chowdhury, G.G., 2003. Natural language processing. Annual review of information science and technology37(1), pp.51-89.

Hirschberg, J. and Manning, C.D., 2015. Advances in natural language processing. Science349(6245), pp.261-266.

Nadkarni, P.M., Ohno-Machado, L. and Chapman, W.W., 2011. Natural language processing: an introduction. Journal of the American Medical Informatics Association18(5), pp.544-551.

 

Neural networks

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature521(7553), 436-444 

Graupe, D. (2013). Principles of artificial neural networks (Vol. 7). World Scientific.

 

Machine learning in Python

Géron, A., 2019. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media.

Raschka, S., 2015. Python machine learning. Packt publishing ltd.

 

Python libraries

https://scikit-learn.org/stable/ 

https://pandas.pydata.org 

This list was last updated on 21/01/2022