emoen/Statistics-and-Data-Analysis-for-Financial-Engineering-Copulas

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emoen/Statistics-and-Data-Analysis-for-Financial-Engineering-Copulas

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Machine-Learning-for-Asset-Managers

Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.

Machine-Learning-for-Asset-Managers-Oslo-Bors

Automatic-interpretation-of-otoliths-using-deep-learning

Recent advances in machine learning have brought forth methods that have been remarkably successful in a variety of settings, most notably in image analysis. These methods are now being applied to data analysis in marine sciences, where they have the potential to automate analysis that previously required manual curation. Here we adapt a machine learning model intended for object recognition to the task of estimating age from otolith images. The model is trained and validated on a collection of otolith images from Greenland halibut. We show that the precision of the model's age estimates is comparable to and may even surpass that of human experts. Age reading from otoliths is an important element in the management of many marine stocks, and automating this analysis is an important step to ensure consistency, lower cost, and increase scale.

Deep-learning-for-regression-of-cod-otoliths

Using the EfficientNet family to predict cod-otolith age.

Jupyter Notebook

stochasticProcessStat220

The course will consider Markov processes in discrete and continuous time. The theory is illustrated with examples from operation research, biology and economy.

Deep-learning-for-salmon-scales

Fish scales constitute a valuable source of information about individual life histories, but correctly extracting this information requires a highly skilled expert. Here, we train a deep convolutional neural network architecture EfficientNet B4 on a set of about 9000 salmon scale images, and show that it attains good performance on predicting a set of variables used in stock management. Further, we see substantial benefits from user transfer learning with a network pre-trained on ImageNet, even if the salmon scale images are very different from those found in the data used for pre-training.

Time_Series_stat211

This course gives an introduction to linear time series models, such as autoregressive, moving average and ARMA models. Moreover, it is shown how the empirical autocorrelation and partial correlation can be used to identify the model. The Durbin- Levinson, the innovation algorithm and the theory for optimal forecasts are explained. The last part of the course gives an introduction to methods of estimation. Empirical modelling using the AIC and FPE criteria is mentioned as is ARCH and GARCH models.

Sentiment-Analysis-with-BERT

https://towardsdatascience.com/sentiment-analysis-in-10-minutes-with-bert-and-hugging-face-294e8a04b671

demo_Marchenko_Pastur_Analysis

Presentation held at IMR Machine Learning journal club 15. October 2020. Demo Marcenko Pasture distribution applied to eigenvalues of random matrix

Jupyter Notebook