Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 74Machine Learning
- 70Reinforcement Learning
- 41Artificial Intelligence
- 36Machine learning
- 22Natural Language Processing
- 22Reinforcement learning
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Spring 2016
Image segmentation is the problem of assigning 2D pixels or 3D voxels to a set of finite labels. In particular, in medical imaging, the goal of segmentation is to partition an MRI or CT image into regions that are relevant to the biology of a particular disease, for example the motor cortex in...
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Fall 2010
Natural Language Processing (NLP) develops computational approaches to processing language data. Supervised machine learning has become the dominant methodology of modern NLP. The performance of a supervised NLP system crucially depends on the amount of data available for training. In the...
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Spring 2011
Standard survival analysis focuses on population-based studies. The objective of our work, survival prediction, is different: to find the most accurate model for predicting the survival times for each individual patient. We view this as a regression problem, where we try to map the features for...
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Fall 2022
Machine learning has been used to solve single-agent search problems. One of its applications is to guide search algorithms by learning heuristics. However, it is difficult to provide guarantees on the quality of learning from a neural network, since the resulting heuristics can be inadmissible,...
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Spring 2022
The concept of state is fundamental to a reinforcement learning agent. The state is the input to the agent's action-selection policy, value functions, and environmental model. A reinforcement learning agent interacts with the environment by performing actions and receiving observations, resulting...
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Fall 2023
The average-reward formulation is a natural and important formulation of learning and planning problems, yet has received much less attention than the episodic and discounted formulations. This dissertation makes three areas of contributions to algorithms and their theories concerning the...