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21 - 40 of 70 search results for Economics Syllabus |u:mlg.eng.cam.ac.uk where 7 match all words and 63 match some words.
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  2. Who owns the atmosphere?

    https://mlg.eng.cam.ac.uk/carl/climate/eacc.html
    4 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.
  3. The Dynamic Beamformer Ali Bahramisharif1,2,�, Marcel A.J. van…

    https://mlg.eng.cam.ac.uk/pub/pdf/BahvanSchGha12.pdf
    13 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.
  4. Addressing Climate Change

    https://mlg.eng.cam.ac.uk/carl/climate/eaccs.html
    4 Jul 2024: Membership immediately creates economic pressure to cut emissions (for all members, not just large emitters).
  5. Beyond Distributive Fairness in Algorithmic Decision Making: Feature…

    https://mlg.eng.cam.ac.uk/adrian/AAAI18-BeyondDistributiveFairness.pdf
    19 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
  6. 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.
  7. 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.
  8. 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
  9. 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
  10. 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.
  11. 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.
  12. 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
  13. 13 Feb 2023: 1978). He-donic prices and the demand for clean air.Journal of Environmental Economics & Man-agement, 5, 81–102.
  14. 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
  15. 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
  16. nips2007-final.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/SilChuGha08.pdf
    13 Feb 2023: This setup is very closely related to the classicseemingly unrelated regressionmodel popular in economics [12].
  17. 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
  18. 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
  19. 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
  20. 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
  21. nips2007-final.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/SilChuGha08.pdf
    27 Jan 2023: This setup is very closely related to the classicseemingly unrelated regressionmodel popular in economics [12].

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