Machine Learning in Finance II

The course covers more advanced methods of machine learning: boosting models, neural networks, and Bayesian time series models. Both theoretical background and practical empirical applications in finance are discussed. Practical part covers problems of regression and classification problems, processing and forecasting of sequences, time-series analysis and deployment of methods in the cloud environment.

- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning. Springer, New York, NY

- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

- Chollet, F. (2017). Deep learning with Python. Manning Publications.

- Stevens, E., Antiga, L., & Viehmann, T. (2020). Deep learning with PyTorch. Manning Publications.

- Intel (2018). Deep learning. Retrieved from https://www.intel.com/content/www/us/en/developer/learn/course-deep-learning.html

- Intel (2018). Time-Series Analysis. Retrieved from https://www.intel.com/content/www/us/en/developer/learn/course-time-series-analysis.html

Along with additional literature assigned to the case studies.

After completing the course, the students will have structured and reliable knowledge on boosting models, neural networks, and Bayesian time series models. They will be able to apply them for both regression and classification problems. They will know the theoretical foundations of these algorithms, as well as have programming skills allowing them to deploy the models in practice, also in the cloud framework. They will also know how to interpret results and explain how they work to other non-technical people.

K_W01, K_U01, K_U02, K_U03, K_U04, K_U05, KS_01.

Preparing two machine learning projects were prepared in groups of at most 2 students - one for regression problem and one for classification. Each project should be prepared on a different dataset selected by the students - one reasonably small dataset and one large dataset - approved by the tutor (for example from https://www.kaggle.com). Students are to prepare a presentation and an extended report in a Python notebook, containing blocks of code that will allow the teacher to fully reproduce the applied analysis.

The following weights are used to determine the final grade:

20% - Presentation

80% - Extended report

The threshold to pass is equal to 60%.