https://dasya.itu.dk/tags/data-science/ Recent content in data science on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Tue, 07 May 2024 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/ajs-debias/ Tue, 07 May 2024 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/ajs-debias/ It has been observed that deep learning models are able to identify patient characteristics such as age, sex, and self-reported race with high accuracy from medical images such as chest x-ray recordings, even when medical doctors cannot. This raises the potential for such models to learn to (falsely) diagnose patients of different demographics differently, even if they present with the same disease characteristics. This may happen because, for example, some groups may tend to be misdiagnosed or underdiagnosed in the datasets using which such models are trained. https://dasya.itu.dk/for-students/proposals/archive/etz-sdf/ Thu, 01 Jun 2023 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/etz-sdf/ Geospatial data refers to information that is tied to specific geographic locations on the Earth’s surface. It includes both the location coordinates (such as latitude, longitude, and, potentially, altitude) and attribute data associated with those locations. Geospatial data is categorized into two types: raster and vector. Vector data represents geographic features as points, lines, and polygons. Points represent individual locations (e.g., cities), lines represent linear features (e.g., roads, rivers), and polygons represent areas (e. https://dasya.itu.dk/for-students/proposals/archive/ajs-concept-bottleneck-models/ Thu, 11 May 2023 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/ajs-concept-bottleneck-models/ Concept Bottleneck Models [1] are designed to leverage high-level concepts. They revisit the classic idea of first predicting concepts that are providing at training time, and then using these concepts to predict the label. By construction, it is possible to intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction. The goal of this project is to leverage the concept block to detect possible spurious correlations (shortcuts) [2,3], memorization [4] or unfair predictions [5]. https://dasya.itu.dk/for-students/proposals/archive/automl-forecasting-energy/ Thu, 20 Aug 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/automl-forecasting-energy/ With the recent hunger for being “data driven”, many organizations are eager for integrating ML in there decision making process. Unfortunately, competent data scientists are still relatively scarce, and manual model development cannot keep up with the demand for magic AI solutions. This is no less true when it comes to forecasting. Knowing the future is extremely handy when making decisions. AutoML for Time Series Forecasting Subsequently many systems are now developing and integrating AutoML tools for time series forecasting. https://dasya.itu.dk/for-students/proposals/archive/non-intrusive-load-monitoring/ Mon, 06 Jul 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/non-intrusive-load-monitoring/ How much does our smart meter readings disclose about us? Can we disentangle the oven from the washing machine from the kettle? Can we identify demographics and behavior patterns from the stream of electricity data? Most danish homes are now equipped so-called “smart meters” - networked electricity meters that report consumption and load at much higher rate than conventional meters. Currently the meters report data back to a centralized datahub on an hourly basis, but the meters are capable of producing data at a much higher granularity than this.