Machine Learning in Finance II
Informacje ogólne
Kod przedmiotu: | 2400-QFU2MLF2 |
Kod Erasmus / ISCED: |
14.3
|
Nazwa przedmiotu: | Machine Learning in Finance II |
Jednostka: | Wydział Nauk Ekonomicznych |
Grupy: |
Anglojęzyczna oferta zajęć WNE UW Przedmioty obowiązkowe dla II roku Quantitative Finance |
Punkty ECTS i inne: |
3.00
|
Język prowadzenia: | angielski |
Rodzaj przedmiotu: | obowiązkowe |
Skrócony opis: |
(tylko po angielsku) 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. |
Pełny opis: |
(tylko po angielsku) The course consists of 3 chapters divided according to the class of presented algorithms: 1) boosting models 2) (deep) neural network models 3) bayesian time series models. It is conducted in the form of interactive laboratories with the use of case studies which are carried out in parallel with the lecture part. Chapter 1. Boosting models 1. AdaBoost 2. Gradient Boosting 3. eXtreme Gradient Boosting 4. Light Gradient Boosting Machine 5. CatBoost Case study - cross sell banking/insurance product - propensity to buy models Chapter 2. Neural network models 1. Multilayer Perceptrons 2. Recurrent Neural Network 3. Convolution Neural Network* 4. Attention mechanism in Neural Network Case study 1 - forecasting the demand for products in large-format stores Case study 2* - car damage classification in the insurance company Chapter 3. Bayesian time series models 1. Facebook Prophet 2. Uber Orbit (as a framework) Case study - forecasting the volume of parcels delivered by a logistics company Chapter 4. Ensembling methods* Project presentation |
Literatura: |
(tylko po angielsku) - 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. |
Efekty uczenia się: |
(tylko po angielsku) 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. |
Metody i kryteria oceniania: |
(tylko po angielsku) 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%. |
Zajęcia w cyklu "Semestr zimowy 2023/24" (zakończony)
Okres: | 2023-10-01 - 2024-01-28 |
Przejdź do planu
PN WT ŚR CZ LAB
LAB
PT |
Typ zajęć: |
Laboratorium, 30 godzin
|
|
Koordynatorzy: | Szymon Lis, Michał Woźniak | |
Prowadzący grup: | Szymon Lis, Michał Woźniak | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Egzamin
Laboratorium - Egzamin |
Właścicielem praw autorskich jest Uniwersytet Warszawski, Wydział Nauk Ekonomicznych.