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Who owns the atmosphere?
https://mlg.eng.cam.ac.uk/carl/climate/eacc.html4 Jul 2024: Such a scheme would immediately put economic pressure on all users to reduce their utilisation of the common atmospheric resource. ... In the following years, low per capita emitters will gain immediate economic benefit from joining. -
The Dynamic Beamformer Ali Bahramisharif1,2,�, Marcel A.J. van…
https://mlg.eng.cam.ac.uk/pub/pdf/BahvanSchGha12.pdf13 Feb 2023: Corresponding author. The authors gratefully acknowledge the support of the Brain-Gain Smart Mix Programme of the Netherlands Ministry of Economic Affairs andthe Netherlands Ministry of Education, Culture and Science. -
Addressing Climate Change
https://mlg.eng.cam.ac.uk/carl/climate/eaccs.html4 Jul 2024: Membership immediately creates economic pressure to cut emissions (for all members, not just large emitters). -
Beyond Distributive Fairness in Algorithmic Decision Making: Feature…
https://mlg.eng.cam.ac.uk/adrian/AAAI18-BeyondDistributiveFairness.pdf19 Jun 2024: Prior work in economics, law, and political science dis-tinguishes between direct and indirect discrimination, sug-gesting that the “wrong” of direct discrimination (which weidentify with violating process fairness) should be ... In Univer-sity of -
Machine Learning 4f13 Lent 2008
https://mlg.eng.cam.ac.uk/teaching/4f13/0708/19 Nov 2023: LECTURE SYLLABUS. Jan 18 . Introduction to Machine Learning(1L): review of probabilistic models, relation to coding terminology: Bayes rule, supervised, unsupervised and reinforcement learning. -
Machine Learning 4f13 Lent 2009
https://mlg.eng.cam.ac.uk/teaching/4f13/0809/19 Nov 2023: LECTURE SYLLABUS. Jan 16 . Introduction to Machine Learning(1L): review of probabilistic models, relation to coding terminology: Bayes rule, supervised, unsupervised and reinforcement learning. -
Machine Learning 4f13 Lent 2012
https://mlg.eng.cam.ac.uk/teaching/4f13/1112/19 Nov 2023: LECTURE SYLLABUS. This year, the exposition of the material will be centered around three specific machine learning areas: 1) supervised non-paramtric probabilistic inference using Gaussian processes, 2) the latent Dirichlet -
Machine Learning 4f13 Lent 2013
https://mlg.eng.cam.ac.uk/teaching/4f13/1213/19 Nov 2023: LECTURE SYLLABUS. This year, the exposition of the material will be centered around three specific machine learning areas: 1) supervised non-paramtric probabilistic inference using Gaussian processes, 2) the TrueSkill ranking -
Machine Learning 4f13 Lent 2011
https://mlg.eng.cam.ac.uk/teaching/4f13/1011/19 Nov 2023: LECTURE SYLLABUS. Jan 20 . Introduction to Machine Learning(1L): review of probabilistic models, relation to coding terminology: Bayes rule, supervised, unsupervised and reinforcement learning. -
Machine Learning 4f13 Lent 2010
https://mlg.eng.cam.ac.uk/teaching/4f13/0910/19 Nov 2023: LECTURE SYLLABUS. Jan 14 . Introduction to Machine Learning(1L): review of probabilistic models, relation to coding terminology: Bayes rule, supervised, unsupervised and reinforcement learning. -
Machine Learning 4f13 Michaelmas 2015
https://mlg.eng.cam.ac.uk/teaching/4f13/1516/19 Nov 2023: LECTURE SYLLABUS. This year, the exposition of the material will be centered around three specific machine learning areas: 1) supervised non-parametric probabilistic inference using Gaussian processes, 2) the TrueSkill ranking -
Healing the Relevance Vector Machine through Augmentation Carl Edward …
https://mlg.eng.cam.ac.uk/pub/pdf/RasQui05.pdf13 Feb 2023: 1978). He-donic prices and the demand for clean air.Journal of Environmental Economics & Man-agement, 5, 81–102. -
Machine Learning 4f13 Lent 2014
https://mlg.eng.cam.ac.uk/teaching/4f13/1314/19 Nov 2023: LECTURE SYLLABUS. This year, the exposition of the material will be centered around three specific machine learning areas: 1) supervised non-paramtric probabilistic inference using Gaussian processes, 2) the TrueSkill ranking -
Machine Learning 4f13 Lent 2015
https://mlg.eng.cam.ac.uk/teaching/4f13/1415/19 Nov 2023: LECTURE SYLLABUS. This year, the exposition of the material will be centered around three specific machine learning areas: 1) supervised non-parametric probabilistic inference using Gaussian processes, 2) the TrueSkill ranking -
nips2007-final.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/SilChuGha08.pdf13 Feb 2023: This setup is very closely related to the classicseemingly unrelated regressionmodel popular in economics [12]. -
Probabilistic Machine Learning 4f13 Michaelmas 2016
https://mlg.eng.cam.ac.uk/teaching/4f13/1617/19 Nov 2023: Lecture Syllabus. This year, the exposition of the material will be centered around three specific machine learning areas: 1) supervised non-parametric probabilistic inference using Gaussian processes, 2) the TrueSkill ranking -
Probabilistic Machine Learning 4f13 Michaelmas 2017
https://mlg.eng.cam.ac.uk/teaching/4f13/1718/19 Nov 2023: Lecture Syllabus. This year, the exposition of the material will be centered around three specific machine learning areas: 1) supervised non-parametric probabilistic inference using Gaussian processes, 2) the TrueSkill ranking -
Probabilistic Machine Learning 4f13 Michaelmas 2018
https://mlg.eng.cam.ac.uk/teaching/4f13/1819/19 Nov 2023: Lecture Syllabus. This year, the exposition of the material will be centered around three specific machine learning areas: 1) supervised non-parametric probabilistic inference using Gaussian processes, 2) the TrueSkill ranking -
Probabilistic Machine Learning 4f13 Michaelmas 2021
https://mlg.eng.cam.ac.uk/teaching/4f13/2122/19 Nov 2023: Lecture Syllabus. This year, the exposition of the material will be centered around three specific machine learning areas: 1) supervised non-parametric probabilistic inference using Gaussian processes, 2) the TrueSkill ranking -
nips2007-final.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/SilChuGha08.pdf27 Jan 2023: This setup is very closely related to the classicseemingly unrelated regressionmodel popular in economics [12].
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