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..
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2015-01-30-the-nips-experiment-examining-the-repeatability-of-peer-review.ipynb
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2015-09-21-peer-review-and-the-nips-experiment.ipynb
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2015-10-06-matrix-factorization.ipynb
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2015-10-07_controlPointTalk.ipynb
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2015-10-13-linear-regression.ipynb
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2015-10-20-basis-functions.ipynb
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2015-10-20-what-kind-of-ai.ipynb
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2015-10-23-what-kind-of-ai.ipynb
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2015-10-27-generalization.ipynb
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2015-11-03-bayesian-regression.ipynb
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2015-11-18-health-infrastructure.ipynb
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2015-11-24-naive-bayes.ipynb
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2015-12-01-logistic-and-glm.ipynb
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2015-12-11-mechanistic-fallacy.ipynb
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2015-12-15-gaussian-processes.ipynb
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2016-01-27-what-kind-of-ai.ipynb
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2016-01-29-OxWaSP GP Talk.ipynb
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2016-03-10_what-kind-of-ai.ipynb
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2016-03-17_what-kind-of-ai-public.ipynb
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2016-05-24-what-kind-of-ai.ipynb
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2016-06-27-data-science-intro.ipynb
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2017-03-16-the-rise-of-the-algorithm.ipynb
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2017-08-30-cloaking-functions.ipynb
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2017-12-04-deep-probabilistic-modelling-with-gaussian-processes.ipynb
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2017-12-04-deep-probabilistic-modelling-with-gaussian-processes.slides.ipynb
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2018-05-02-towards-ml-systems-design.ipynb
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2018-05-11-outlook-for-uk-ai-and-ml.ipynb
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2018-05-29-uncertainty-in-loss-functions.ipynb
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2018-06-04-bayesian-methods.ipynb
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2018-06-04-bayesian-methods.slides.ipynb
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2018-08-25-probabilistic-machine-learning.ipynb
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2018-08-25-probabilistic-machine-learning.slides.ipynb
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2018-09-03-gpss-session-1.ipynb
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2018-11-14-bayesian-methods-abuja.ipynb
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2018-11-30-artificial-intelligence-data-science-and-machine-learning-systems-design.ipynb
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2018-12-10-machine-learning-and-the-physical-world.ipynb
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2019-01-09-gaussian-processes.ipynb
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2019-01-09-gaussian-processes.slides.ipynb
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2019-01-11-deep-gaussian-processes.ipynb
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2019-01-11-deep-gaussian-processes.slides.ipynb
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2019-05-23-meta-modelling-and-deploying-ml-software.ipynb
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2019-06-03-what-is-machine-learning.ipynb
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2019-06-06-the-three-ds-of-machine-learning.ipynb
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2019-09-10-introduction-to-deep-gps.ipynb
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2019-10-15-data-quality-and-data-readiness.ipynb
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2019-10-21-what-is-machine-learning-ashesi.ipynb
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2020-01-24-r250-gp-intro.ipynb
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2020-07-24-ml-systems.ipynb
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2020-08-23-modelling-things.ipynb
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2020-09-16-deep-gps.ipynb
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2021-02-02-introduction-to-machine-intelligence.ipynb
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2021-05-05-ml-and-the-physical-world-sheffield.ipynb
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2021-05-17-post-digital-transformation-intellectual-debt.ipynb
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2021-06-16-the-neurips-experiment.ipynb
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2021-07-07-ml-and-the-physical-world-trustworthy-ai.ipynb
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2021-07-13-ml-and-the-physical-world-tuebingen.ipynb
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2021-09-15-emulation.ipynb
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2021-11-04-deep-gaussian-processes-a-motivation-and-introduction.ipynb
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2021-11-11-data-first-culture-post-digital-transformation-and-intellectual-debt.ipynb
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2022-04-26-post-digital-transformation-decision-making-and-intellectual-debt.ipynb
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2022-05-10-the-neurips-experiment-snsf.ipynb
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2022-06-06-deep-gaussian-processes-a-motivation-and-introduction-bristol.ipynb
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2022-06-09-data-first-culture-post-digital-transformation-and-intellectual-debt-june-22.ipynb
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2022-06-17-deep-gaussian-processes-a-motivation-and-introduction-sheffield.ipynb
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2022-07-07-data-first-culture-post-digital-transformation-and-intellectual-debt-july-22.ipynb
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2022-09-13-emulation-2022.ipynb
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2022-11-10-data-first-culture-november-22.ipynb
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2022-11-14-how-engineers-solve-big-and-difficult-problems-part-1-the-challenge-opportunities-presented-to-engineers-by-ai-ml.ipynb
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2023-06-08-data-first-culture-june-23.ipynb
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2023-11-09-data-first-culture-november-23.ipynb
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2023-11-21-how-do-we-cope-with-rapid-change-like-ai-ml.ipynb
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2024-03-12-the-atomic-human-st-andrews.ipynb
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2024-03-28-the-atomic-human-vector.ipynb
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gp_tutorial.py
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mountain_car.py
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teaching_plots.py
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