Cover image of Computer Science
(4)
Education
Technology
Courses

Computer Science

Updated 7 days ago

Education
Technology
Courses
Read more

This series is host to episodes created by the Department of Computer Science, University of Oxford, one of the longest-established Computer Science departments in the country. The series reflects this department's world-class research and teaching by providing talks that encompass topics such as computational biology, quantum computing, computational linguistics, information systems, software verification, and software engineering.

Read more

This series is host to episodes created by the Department of Computer Science, University of Oxford, one of the longest-established Computer Science departments in the country. The series reflects this department's world-class research and teaching by providing talks that encompass topics such as computational biology, quantum computing, computational linguistics, information systems, software verification, and software engineering.

iTunes Ratings

4 Ratings
Average Ratings
3
0
1
0
0

iTunes Ratings

4 Ratings
Average Ratings
3
0
1
0
0
Cover image of Computer Science

Computer Science

Latest release on Dec 11, 2019

The Best Episodes Ranked Using User Listens

Updated by OwlTail 7 days ago

Rank #1: Strachey Lecture - Probabilistic machine learning: foundations and frontiers

Podcast cover
Read more
Professor Zoubin Ghahramani gives a talk on probabilistic modelling from it's foundations to current areas of research at the frontiers of machine learning. Probabilistic modelling provides a mathematical framework for understanding what learning is, and has therefore emerged as one of the principal approaches for designing computer algorithms that learn from data acquired through experience. Professor Ghahramani will review the foundations of this field, from basics to Bayesian nonparametric models and scalable inference. He will then highlight some current areas of research at the frontiers of machine learning, leading up to topics such as probabilistic programming, Bayesian optimisation, the rational allocation of computational resources, and the Automatic Statistician.

The Strachey lectures are generously supported by OxFORD Asset Management.

Mar 15 2017

50mins

Play

Rank #2: Lovelace Lecture: Learning and Efficiency of Outcomes in Games

Podcast cover
Read more
Éva Tardos, Department of Computer Science, Cornell University, gives the 2017 Ada Lovelace Lecture on 6th June 2017. Selfish behaviour can often lead to suboptimal outcome for all participants, a phenomenon illustrated by many classical examples in game theory. Over the last decade we developed good understanding on how to quantify the impact of strategic user behaviour on the overall performance in many games (including traffic routing as well as online auctions). In this talk we will focus on games where players use a form of learning that helps themadapt to the environment, and consider two closely related questions: What are broad classes of learning behaviours that guarantee that game outcomes converge to the quality guaranteed by the price of anarchy, and how fast is this convergence. Or asking these questions more broadly: what learning guarantees high social welfare in games, when the game or the population of players is dynamically changing.

Aug 22 2017

56mins

Play

Rank #3: Strachey Lecture - Quantum Supremacy

Podcast cover
Read more
Dr Scott Aaronson (MIT, UT Austin) gives the 2016 Strachey lecture. In the near future, it will likely become possible to perform special-purpose quantum computations that, while not immediately useful for anything, are plausibly hard to simulate using a classical computer. These "quantum supremacy experiments" would be a scientific milestone---decisively answering quantum computing skeptics, while casting doubt on one of the foundational tenets of computer science, the Extended Church-Turing Thesis. At the same time, these experiments also raise fascinating questions for computational complexity theorists: for example, on what grounds should we believe that a given quantum system really is hard to simulate classically?

Does classical simulation become easier as a quantum system becomes noisier? and how do we verify the results of such an experiment? In this lecture, I'll discuss recent results and open problems about these questions, using three proposed "quantum supremacy experiments" as examples: BosonSampling, IQP / commuting Hamiltonians, and random quantum circuits.

Based partly on joint work with Alex Arkhipov and with Lijie Chen.

The Strachey Lectures are generously supported by OxFORD Asset Management.

Jun 14 2016

1hr 12mins

Play

Rank #4: Strachey Lecture - The Once and Future Turing

Podcast cover
Read more
Professor Andrew Hodges author of 'Alan Turing: The Enigma' talks about Turing's work and ideas from the definition of computability, the universal machine to the prospect of Artificial Intelligence. In 1951, Christopher Strachey began his career in computing. He did so as a colleague of Alan Turing, who had inspired him with a 'Utopian' prospectus for programming. By that time, Turing had already made far-reaching and futuristic innovations, from the definition of computability and the universal machine to the prospect of Artificial Intelligence. This talk will describe the origins and impacts of these ideas, and how wartime codebreaking allowed theory to turn into practice. After 1951, Turing was no less innovative, applying computational techniques to mathematical biology. His sudden death in 1954 meant the loss of most of this work, and its rediscovery in modern times has only added to Turing's iconic status as a scientific visionary seeing far beyond his short life.
Andrew Hodges is the author of Alan Turing: The Enigma (1983), which inspired the 2014 film The Imitation Game.
The Strachey Lectures are generously supported by OxFORD Asset Management.

Nov 02 2016

1hr 7mins

Play

Rank #5: Strachey Lecture - Computer Agents that Interact Proficiently with People

Podcast cover
Read more
Professor Kraus will show how combining machine learning techniques for human modelling, human behavioural models, formal decision-making and game theory approaches enables agents to interact well with people. Automated agents that interact proficiently with people can be useful in supporting, training or replacing people in complex tasks. The inclusion of people presents novel problems for the design of automated agents’ strategies. People do not necessarily adhere to the optimal, monolithic strategies that can be derived analytically. Their behaviour is affected by a multitude of social and psychological factors.  In this talk I will show how combining machine learning techniques for human modelling, human behavioural models, formal decision-making and game theory approaches enables agents to interact well with people. Applications include intelligent agents.
 
The Strachey Lectures are generously supported by OxFORD Asset Management.

Jun 23 2017

40mins

Play