Welcome to U of T

U of T's origins date back to 1827, when John Strachan, a leader of Upper Canada's Anglican elite, obtained a royal charter for King's College, the colony's first institution of higher learning. The new university, controlled by the Church of England, offered its first courses in 1843 and granted its first degrees a year later. King's College was secularized by the provincial government in 1849 and renamed the University of Toronto, designating it the "provincial university."

Chronology of the University of Toronto

In 1887, provincial legislation authorized the federation of other colleges and universities with the University of Toronto. Victoria University, Knox and Wycliffe colleges joined in 1889, followed by Trinity in 1904 and St. Michael's in 1911. This arrangement guaranteed the colleges their autonomy and character while providing students with access to the facilities of a larger university.

University of Toronto Buildings

Burwash Hall The Croft House Trinity College Fall scene Winter at U of T An old U of T building Trinity College The Campus

U of T Computer Science

Artificial Intelligence

The Artificial Intelligence group works in the three primary subareas of the field -- computational linguistics, knowledge representation and reasoning, and computer vision -- and also in additional subareas such as intelligent information systems and neural networks. As well as regular government grants, the group has received much support in recent years from the Canadian Institute for Advanced Research. The high quality of the group is well documented; in recent years, its members have received two Steacie Fellowships, two major awards from IJCAI, and several best-paper prizes at conferences. Many graduates of the group are now themselves distinguished AI researchers.

Computational Linguistics

Computational linguistics: The subarea of AI concerned with human languages ("natural languages") is computational linguistics. Researchers in this area are interested in developing programs that can "understand" and generate natural language. "Understanding" involves parsing linguistic input, determining its literal and non-literal meaning, and representing the meaning in a computational formalism; generation reverses this process. Research in this area is now being applied in commercial systems for tasks such as automatic or semi-automatic translation from one language to another, information retrieval, and intelligent aids to writers.

Knowledge Representation and Reasoning

Knowledge representation and reasoning: A theme common in much AI research is that to behave intelligently, computers must come to "know" a good deal of what every human being knows about the world and the organisms that inhabit it. Knowledge representation and reasoning (or KR) is the study of how to impart this knowledge to a computer: how do we write down descriptions of the world in such a way that a computer would be able to draw appropriate conclusions about the world by manipulating them?

Computational Vision

Computational vision: The long-term goal of research in computational vision is to understand the visual information that is represented in images and image sequences. By quot;understanding", we mean that a computer system viewing an image could report on the contents of an image in a useful manner, where utility may be measured by specific task or by the standards of human perception. Research in the field ranges from practical industrial vision applications to the design and construction of robotic vision sensors such as stereo heads to attempts to understand how the human brain processes and uses visual information. As a result, there are many subareas of research within computational vision, including edge detection, segmentation, texture analysis, colour perception, stereo tracking, perceptual organization, object recognition, active and attentive vision, sensor design, motion analysis, event perception, learning, and so on. Impressive successes have been seen, but the research area contains a large number of open problems, making this a challenging topic for many years to come.

Cognitive Robotics

Here at the University of Toronto, the work of Reiter and Levesque most recently has been concerned with "cognitive botics", that is, KR from the point of view of an autonomous robot interacting with a dynamic and incompletely known world. Among other things, this has required developing new accounts of the relationship among the knowledge, perception, and action of such a robot.

Neural Networks

This group invents and evaluates novel learning algorithms for neural networks. Our main aims are to gain insight into how the brain learns, to develop algorithms that are useful for practical tasks, and to understand the relationship of neural network algorithms to other statistical approaches.

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