In the United States, institutional investors submit 13F reports to SEC every quarter, disclosing the number of shares they own. However, investors may delay filing these reports to conceal their strategies. In this study, data from 13F filings were enriched with additional information such as stock prices and industries to create a bipartite, dynamic, and rich-attributed graph. The changes in network metrics, edge counts, and motif counts were visualized, highlighting crises in the relevant time period. The study found that clustering coefficient, significant position changes, late-filed 13F reports, and specific motif counts of graph shows significant changes during market crashes.
Date: 18.01.2024 / 10:00 Place: A-212
This list will be updated as the information about the missing courses are provided.
Cancers of unknown primary (CUP) pose treatment challenges due to unidentified primary sites, affecting survival rates significantly. Traditional methods falter in pinpointing origins. Gene expression-based studies offer promise; machine learning models trained on annotated cancer data achieve remarkable accuracy, surpassing conventional methods. Our study, utilizing three machine learning models trained on TCGA data, attained competitive accuracy, around 96%.
Date: 18.01.2024 / 11:00 Place: A-108
Despite impressive achievements made by machine learning algorithms (especially in deep learning), they are easily tricked by modified input data. Adversarial attacks target machine learning models severely. Adversarial training is an effective method against adversarial attacks, but it is not suitable for network domains due to network flow characteristics. In this thesis, the autoencoder's reconstruction error is used for detecting adversarial attacks. The IDS model, RAIDS is proposed and achieves respectful results against adversarial attacks.
Date: 17.01.2024 / 14:00 Place: A-108
Global climate change induces lake level fluctuations, impacted by evolving meteorological factors and water use. Input or output changes swiftly affect the water balance equation. This study explores predictive models for climatic and hydrologic variables, assessing their correlations with lake water level and water quality. Using diverse algorithms—Naive Method, ANN, and RNN—LSTM excels in accuracy by RMSE. Comparisons with the Naïve Method confirm ANN and RNN predictive prowess, especially with extended horizons. Correlations with temperature and evaporation highlight lake water quality impacts. Models and metrics construct a decision support tool for water managers.
Date: 19.12.2023 / 13:30 Place: A-212
This thesis applies machine learning to predict outcomes of men’s singles tennis matches from 2009-2022, utilizing a standardized data mining framework, namely SRP-CRISP-DM, for replicable results. Employing six feature extraction techniques, three models, and two feature selection methods with time-based cross-validation and hyperparameter tuning, the Extreme Gradient Boosting model emerged as the top performer, scoring a Brier score of 0.1913 and an accuracy of 70.5%, with bookmakers' odds as the top predictive feature.
Date: 07.12.2023 Place: A-212
You can find the Preliminary Evaluation Results at the link below:
You can find the Preliminary Evaluation Results at the link below:
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