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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 -
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 -
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 -
From Parity to Preference-based Notionsof Fairness in Classification…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS17-from-parity-to-preference.pdf16 May 2024: In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and proposepreference-based notions of fairness—given the choice between various sets ... In this work, we introduce, formalize and
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