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43:24 Data literacy for beginners (updated 2022-2023) Data literacy for beginners (updated 2022-2023)	Data literacy for beginners (updated 2022-2023) Data Literacy for beginners is a one-hour webinar designed to introduce staff and students to the concepts first published in a popular statistics pamphlet, entitled How To Lie with Statistics, by… data skills digital skills and training sample population statistics bias chart numbers size graph chapter skills values students error sampling chat statistic axis trends market samples From Tracy Steinberg 0 likes 14 plays 0 Publisher Tracy Steinberg Licence Type All rights reserved The University of Edinburgh Language British English Date Created November 1st, 2022
33:15 AML: Model Selection - Statistical Tests AML: Model Selection - Statistical Tests	AML: Model Selection - Statistical Tests Applied Machine Learning - INFR11211 AML: Model Selection - Statistical Tests Part 2/2 aml machine learning model evaluation distribution hypothesis samples population sample difference variance things splits errors error dataset tests model student values significance test means statistics From Siddharth N 0 likes 126 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
19:02 AML: Evaluation - Regression AML: Evaluation - Regression	AML: Evaluation - Regression Applied Machine Learning - INFR11211 AML: Evaluation - Regression Part 2/2 aml machine learning evaluation regression error model values correlation coefficient target variance dataset hat median prediction things points fact predictions fit square consequence difference From Siddharth N 0 likes 93 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
20:16 AML: Generalisation - Measuring Generalisation AML: Generalisation - Measuring Generalisation	AML: Generalisation - Measuring Generalisation Applied Machine Learning - INFR11211 AML: Generalisation - Measuring Generalisation Part 2/3 model error test training variance distribution labels targets hyperparameters prediction expectation predictions validation target values generalisation bias things dataset partition From Siddharth N 0 likes 133 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
11:22 AML: Generalisation - What is it? AML: Generalisation - What is it?	AML: Generalisation - What is it? Applied Machine Learning - INFR11211 AML: Generalisation - What is it? Part 1/3 model capacity training regularities test error degree parameters generalisation polynomial flexibility point space examples lot reason linear hyperparameter hyperparameters dataset From Siddharth N 0 likes 100 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
15:30 AML: Optimisation - Stochastic Gradient Descent AML: Optimisation - Stochastic Gradient Descent	AML: Optimisation - Stochastic Gradient Descent Applied Machine Learning - INFR11211 AML: Optimisation - Stochastic Gradient Descent Part 3/3 gradient descent points point things direction gradients error minimum function step dataset problems estimate weight variance estimator space sum loss From Siddharth N 0 likes 108 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
15:58 AML: Optimisation - Gradient Descent AML: Optimisation - Gradient Descent	AML: Optimisation - Gradient Descent Applied Machine Learning - INFR11211 AML: Optimisation - Gradient Descent Part 2/3 aml machine learning optimisation gradients function gradient step direction error size weight times algorithm rate point weights optimization region learning perturbation steps taylor descent From Siddharth N 0 likes 109 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
10:18 AML: Optimisation - ML Optimisation AML: Optimisation - ML Optimisation	AML: Optimisation - ML Optimisation Applied Machine Learning - INFR11211 AML: Optimisation - ML Optimisation Part 1/3 aml machine learning optimisation error function log gradient weights regression things weight edit probability linear perspective model direction values vector settings errors points functions From Siddharth N 0 likes 116 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
01:14:38 Anamaria Costache - Challenges and open problems… Anamaria Costache - Challenges and open problems in Fully Homomorphic Encryption	Anamaria Costache - Challenges and open problems in Fully Homomorphic Encryption Subtitles will be added at a later date. 27 July 2022 Foundations and Applications of Lattice-based Cryptography Anamaria Costache, Norwegian University of Science and Technology Challenges and open… things lot ciphertext encryption point key multiplication bit level error schemes thing scheme elements modulus polynomial ciphertexts decryption fact questions From Liam Holligan 0 likes 33 plays 0 Publisher Liam Holligan Licence Type All rights reserved Language English Date Created August 2nd, 2022 Retain Source File Yes
01:21:17 Ilaria Chillotti - Introduction to FHE and the… Ilaria Chillotti - Introduction to FHE and the TFHE Scheme (Pt 2)	Ilaria Chillotti - Introduction to FHE and the TFHE Scheme (Pt 2) Subtitles will be added at a later date. 26 July 2022 Foundations and Applications of Lattice-based Cryptography Ilaria Chillotti, ZAMA Introduction to FHE and the TFHE Scheme (Pt 1) times ciphertext bootstrapping encryption function ring multiplication error power delta lwe bit position key operation practise question rotation result minus From Liam Holligan 0 likes 35 plays 0 Publisher Liam Holligan Licence Type All rights reserved Language English Date Created August 2nd, 2022 Retain Source File Yes
58:29 Ilaria Chillotti - Introduction to FHE and the… Ilaria Chillotti - Introduction to FHE and the TFHE Scheme (Pt 1)	Ilaria Chillotti - Introduction to FHE and the TFHE Scheme (Pt 1) Subtitles will be added at a later date. 26 July 2022 Foundations and Applications of Lattice-based Cryptography Ilaria Chillotti, ZAMA Introduction to FHE and the TFHE Scheme (Pt 1) encryption key operations secret box cloud bit operation problem question bootstrapping times error ciphertext thermometer solution decryption point parameters multiplication From Liam Holligan 0 likes 52 plays 0 Publisher Liam Holligan Licence Type All rights reserved Language English Date Created August 2nd, 2022 Retain Source File Yes
19:07 The Benefits of an Open-Science Approach in … The Benefits of an Open-Science Approach in Student Research Projects - Emma MacKenzie & Felicity Anderson	The Benefits of an Open-Science Approach in Student Research Projects - Emma MacKenzie & Felicity Anderson Student projects can sometimes be overlooked when considering open science methods. This is perhaps because of the limited time available to complete the project, a lack of confidence in their open… open research eor conference presentation lab students projects student science skills things benefits tools goals tasks kind pi problems thing experience approach error tickets issue From Kerry Miller 0 likes 9 plays 0 Publisher Edinburgh Open Research Licence Type Creative Commons - Attribution Language English Date Created May 27th, 2022
41:51 A Theoretically Tractable Framework for K Fold… A Theoretically Tractable Framework for K Fold Cross Vaidation - Sabyasachi Chatterjee	A Theoretically Tractable Framework for K Fold Cross Vaidation - Sabyasachi Chatterjee Structural Breaks and Shape Constraints A Theoretically Tractable Framework for K Fold Cross Vaidation Sabyasachi Chatterjee (University of Illinois at Urbana-Champaign) 19 May 2022 icms lambda hat sample estimator risk cross completion prediction estimators validation lasso folds tuning error framework fold inequality predictions problem term From Iain Cms 0 likes 23 plays 0 Licence Type All rights reserved Language English Date Created May 23rd, 2022
33:48 Anisotropic, Adaptive Finite Elements for… Anisotropic, Adaptive Finite Elements for Academic and Industrial Problems - Marco Picasso	Anisotropic, Adaptive Finite Elements for Academic and Industrial Problems - Marco Picasso Adaptive Moving and Anisotropic Meshes for the Numerical Approximation of PDEs Anisotropic, Adaptive Finite Elements for Academic and industrial Problems Marco Picasso (EPFL) 12 May 2022 icms mesh solution error equation estimator term thing derivative scheme interpolation times difference function elements stokes process computation space pressure timestamp From Iain Cms 0 likes 18 plays 0 Licence Type All rights reserved Language English Date Created May 16th, 2022
41:28 Accelerating Mesh Adaptation using Neural… Accelerating Mesh Adaptation using Neural Networks - Joseph Wallwork	Accelerating Mesh Adaptation using Neural Networks - Joseph Wallwork Adaptive Moving and Anisotropic Meshes for the Numerical Approximation of PDEs Accelerating Mesh Adaptation using Neural Networks Joseph Wallwork (Imperial College London) 12 May 2022 icms mesh solution error estimation adaptation pde hand side goal cases equation problem approach test networks function element input accuracy method From Iain Cms 0 likes 15 plays 0 Licence Type All rights reserved Language English Date Created May 16th, 2022
31:07 Mesh Adaptation for High-Order Flow Simulations -… Mesh Adaptation for High-Order Flow Simulations - Kaloyan Kirilov	Mesh Adaptation for High-Order Flow Simulations - Kaloyan Kirilov Adaptive Moving and Anisotropic Meshes for the Numerical Approximation of PDEs Mesh Adaptation for High-Order Flow Simulations Kaloyan Kirilov (Imperial College London) 12 May 2022 icms mesh shock adaptation patient bit degrees freedom element kind regions jacobian sensor discontinuities linear solution radius shocks error variation lot From Iain Cms 0 likes 19 plays 0 Licence Type All rights reserved Language English Date Created May 16th, 2022

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