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Skip to Search Results- 29Greiner, Russell (Computing Science)
- 14Schuurmans, Dale (Computing Science)
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- 3Bowling, Michael (Computing Science)
- 1Bolduc, Francois (Pediatrics)
- 1Bowling, Mike (Computing Science)
- 13Machine learning
- 9Machine Learning
- 3Reinforcement Learning
- 2Diagnosis
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- 2Functional magnetic resonance imaging
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Spring 2010
This thesis presents Pathway Informed Analysis (PIA), a classification method for predicting disease states (diagnosis) from metabolic profile measurements that incorporates biological knowledge in the form of metabolic pathways. A metabolic pathway describes a set of chemical reactions that...
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Spring 2010
In this work, we present a unified, general approach to variance reduction in agent evaluation using machine learning to minimize variance. Evaluating an agent's performance in a stochastic setting is necessary for agent development, scientific evaluation, and competitions. Traditionally,...
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Fall 2009
For zero-sum games, we have efficient solution techniques. Unfortunately, there are interesting games that are too large to solve. Here, a popular approach is to solve an abstract game that models the original game. We assume that more accurate the abstract games result in stronger strategies....
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Addressing the Challenges of Applying Machine Learning for Predicting Mental Disorders and Their Prognosis Using Two Case Studies
DownloadSpring 2019
Ghoreishiamiri, Seyedehreyhaneh
One of the principal applications of machine learning in psychiatry is to build automated tools that can help clinicians predict the diagnosis and prognosis of mental disorders using available data from patients’ profiles. Here, in two different studies, we investigate ways to use machine learn-...
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Advances in Probabilistic Generative Models: Normalizing Flows, Multi-View Learning, and Linear Dynamical Systems
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This thesis considers some aspects of generative models including my contributions in deep probabilistic generative architectures and linear dynamical systems. First, some advances in deep probabilistic generative models are contributed. Flow-based generative modelling is an emerging and highly...
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Spring 2023
Reinforcement learning (RL) defines a general computational problem where the learner must learn to make good decisions through interactive experience. To be effective in solving this problem, the learner must be able to explore the environment, make accurate predictions about the future, and...
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Advancing ECG Analysis through Machine Learning: A Study on Data Generation for ECG Classification and Feature Selection For Individual Survival Prediction
DownloadSpring 2024
Electrocardiograms (ECGs) are a valuable and easily-collected measurement of heart health, reflecting its morphology (R peak, QRS duration,..) and rhythm(sequence of multiple heartbeats). With the advance of machine learning, many studies utilize electrocardiogram (ECG) signals for various...
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Spring 2019
Forouzandehmoghadam, Amirhosein
A biomarker is a feature (e.g., gene expression, SNP, etc.) that is significantly different between two classes of instances – typically case and control. Knowing these biomarkers can help us understand a biological condition or identify the appropriate treatment for a certain disease. Many...
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Assessing the Feasibility of Learning Biomedical Phenotype Patterns Using High-Throughput Omics Profiles
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A decade after the completion of the human genome project, the rapid advancement of the high-throughput measurement technologies has made omics (genomics, epigenomics, transcriptomics, metabolomics) profiling feasible. The availability of such omics profiles has raised the hope for the...
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Fall 2013
Due to its wide application in various fields, clustering, as a fundamental unsupervised learning problem, has been intensively investigated over the past few decades. Unfortunately, standard clustering formulations are known to be computationally intractable. Although many convex relaxations of...