This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.
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
- 2Reinforcement Learning
- 1General Value Functions
- 1Knowledge
- 1Off-policy Learning
- 1Policy Evaluation
- 1Reinforcement learning
-
Fall 2015
Understanding how an artificial agent may represent, acquire, update, and use large amounts of knowledge has long been an important research challenge in artificial intelligence. The quantity of knowledge, or knowing a lot, may be nicely thought of as making and updat- ing many predictions about...
-
Fall 2011
We present a new family of gradient temporal-difference (TD) learning methods with function approximation whose complexity, both in terms of memory and per-time-step computation, scales linearly with the number of learning parameters. TD methods are powerful prediction techniques, and with...
-
Fall 2018
In this thesis, we make two contributions in learning with artificial neural networks. Artificial neural networks have made great success in various challenging domains. Our first contribution is a new technique named cross-propagation that does cross-validation online. In cross-validation,...
-
Fall 2018
Knowledge is central to intelligence. Intelligence can be thought of as the ability to acquire knowledge and apply it effectively. Despite being a subject of intense interest in artificial intelligence, it is not yet clear what the best approach is for an intelligent system to acquire and...