Inaugural Lecture of Professor Simon Maskell
6:00pm - 8:00pm /
Thursday 18th October 2018
/ Venue: ELT Electrical Engineering & Electronics
Type: Lecture /
Category: Department
- 0151 795 4297
- Jane Gallagher
- Suitable for: Staff, students and anyone with an interest in the subject
- Admission: Free but booking required
- Book now
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Samples of the Past, Present and an Uncertain Future
We live in an uncertain world. Our experiences of the world give us clues as to what is happening. This inaugural lecture focuses on the process of using those experiences to improve our understanding of the world. The mathematics of solving that generic challenge is relevant to a wealth of specific problems. The mathematical solution demands that we need two kinds of model: one for how our experiences relate to the world and another for how the world evolves over time. Each of these models capture two kinds of understanding of the world: how the world is predictable and how it can behave in unexpected ways. The better the models the better the theoretical ability of the maths to provide useful information (so long as the better models are right). To turn that theoretical ability into practical utility, we need an algorithm. Thankfully, there is one algorithm that can be used whatever models are considered. That algorithm boils down to guessing: so long as the guessing isn’t too narrow minded, the more guesses you have, the better you can do (and big computers can do a lot of guessing). It transpires that this approach makes it possible to use previous guesses of what was happening to inform guesses of what is happening now. The same guesses can be extrapolated into the uncertain future and a related approach can be used to learn: to refine previous guesses as our experiences grow. This lecture will walk through this process with a focus on processing a video of a moving tennis ball to try to estimate its current velocity, predict where it will hit the ground and estimate the spin that the ball was hit with.
We live in an uncertain world. Our experiences of the world give us clues as to what is happening. This inaugural lecture focuses on the process of using those experiences to improve our understanding of the world. The mathematics of solving that generic challenge is relevant to a wealth of specific problems. The mathematical solution demands that we need two kinds of model: one for how our experiences relate to the world and another for how the world evolves over time. Each of these models capture two kinds of understanding of the world: how the world is predictable and how it can behave in unexpected ways. The better the models the better the theoretical ability of the maths to provide useful information (so long as the better models are right). To turn that theoretical ability into practical utility, we need an algorithm. Thankfully, there is one algorithm that can be used whatever models are considered. That algorithm boils down to guessing: so long as the guessing isn’t too narrow minded, the more guesses you have, the better you can do (and big computers can do a lot of guessing). It transpires that this approach makes it possible to use previous guesses of what was happening to inform guesses of what is happening now. The same guesses can be extrapolated into the uncertain future and a related approach can be used to learn: to refine previous guesses as our experiences grow. This lecture will walk through this process with a focus on processing a video of a moving tennis ball to try to estimate its current velocity, predict where it will hit the ground and estimate the spin that the ball was hit with.