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Spring 2024
In reinforcement learning, the notion of state plays a central role. A reinforcement learning agent requires the state to evaluate its current situation, select actions, and construct a model of the environment. In the classic setting, it is assumed that the environment provides the agent with...
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Spring 2024
As cancer is the leading global cause of death, an ongoing challenge is predicting an individual's cancer progression accurately, to facilitate personalized treatment planning. Individuals diagnosed with cancer may succumb to the illness or face cancer recurrence post-treatment. The first part of...
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Time Series and Machine Learning Approach for Forecasting the Demand for Small Equipment, Tools, and Consumables for Industrial Construction Projects
DownloadSpring 2024
The high consumption and utilization of demand for small equipment, tools, and consumables in construction projects underscores the necessity for effective procurement strategies. Accurate estimation of these consumables is crucial for moving toward project completion in a timely manner. With...
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Statistical Learning with Many Variables as Covariates or Outcomes: Association Inference and Prediction of Late effects of Childhood Cancer and Its Treatment
DownloadSpring 2024
Advancements in childhood cancer treatment have increased the 5-year survival rates substantially, from 20% in 1950-1954 to over 85% currently. While this success is a remarkable accomplishment in oncology, it concurrently introduces a new concern, namely, the emergence of late adverse effects,...
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Spring 2024
Despite recent advancements in molecular dynamics (MD) methods, the computational costs of \emph{ab initio} molecular dynamics simulations for explicit solvation systems are still too significant. If accuracy is to be left uncompromised, new methods must be employed to reduce computational...
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Improving the reliability of reinforcement learning algorithms through biconjugate Bellman errors
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In this thesis, we seek to improve the reliability of reinforcement learning algorithms for nonlinear function approximation. Semi-gradient temporal difference (TD) update rules form the basis of most state-of-the-art value function learning systems despite clear counterexamples proving their...
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Spring 2024
In model-based reinforcement learning, an agent can improve its policy by planning: learning from experience generated by a model. Search control is the problem of determining which starting state should be used to generate this experience. Given a limited planning budget, an agent should be...
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In-silico Methods for Drug Discovery: Applications of Molecular Dynamics, Drug Docking, and Machine Learning
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Drug discovery is a venture that is costly in both time and money. In-silico methods are a core part of biomedical research, from traditional tools such as drug docking and molecular dynamics to newer machine learning frameworks, all of which are more efficient in both time and cost compared to...
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Technical Efficiency of Wildfire Detection and Machine Learning Predictions in Alberta, Canada
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Wildfire management agencies must continue to evolve and adjust to the dynamic nature of their industry. They face pressures that include a changing climate with the prospect of intense future fire seasons, tighter government budgets for wildfire detection and suppression, and the fast-pace of...
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Spring 2024
This thesis aims to create a platform to estimate and monitor the University of Alberta (UAlberta) fleet vehicles’ fuel consumption and Carbon Dioxide (CO2) emissions. The main objective is to collect and analyze fleet vehicles information to reduce energy consumption and greenhouse gas emissions...