Search for tag: "classification"

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12:37 Hyperbolic PDE Analytical methods Part 5: Formal… Hyperbolic PDE Analytical methods Part 5: Formal classification of PDEs	Hyperbolic PDE Analytical methods Part 5: Formal classification of PDEs Derive and discuss the formal classification for second order partial differential equations pde3 engineering mathematics classification lecture From Daniel Friedrich on February 22nd, 2021 0 likes 28 plays 0 Course Code SCEE09004 Publisher Daniel Friedrich Licence Type Creative Commons - Attribution Language English Date Created February 20th, 2021
17:50 FDS-S2-02-2-1 Principle of logistic regression FDS-S2-02-2-1 Principle of logistic regression	FDS-S2-02-2-1 Principle of logistic regression We introduce the principle of logisitc regression, illustrating its application to a credit approval dataset. We also introduce the concepts of odds and odds ratios. inf2-fds regression odds probability beta approval success x. age variable function coefficients variables credit ratio minus curve classification naught linear times From David Sterratt on January 19th, 2021 0 likes 184 plays 0 Course Code INFR08030 Publisher David Sterratt Licence Type Creative Commons - Attribution Non Commercial No Derivatives Language English Date Created January 19th, 2021
50:02 Ergodic theory without invariant measures -… Ergodic theory without invariant measures - Stefaan Vaes	Ergodic theory without invariant measures - Stefaan Vaes EMS talk Stefaan Vaes (KU Leuven) Ergodic theory without invariant measures 11 December 2020 To remove the captions from this video press CC on the bottom toolbar. mathematics measure transformations space transformation action probability orbit groups equivalence measures classification cycle algebra relation infinity invariant point theorem neumann theory From GILLIAN KERR on December 11th, 2020 0 likes 26 plays 0 Licence Type All rights reserved Language english Date Created December 11th, 2020
51:21 MLP Q&A 2 - Session 5 (Week 11) MLP Q&A 2 - Session 5 (Week 11)	MLP Q&A 2 - Session 5 (Week 11) Machine Learning Practical Q&A 2 - Session 5 (Week 11). machine learning deep learning question network problem class pixels thing background classes backgrounds things object validation image sets segmentation images classification units kind questions From Pavlos Andreadis on December 2nd, 2020 1 likes 5 plays 0 Course Code INFR11132 Licence Type All rights reserved Language English
55:00 November 25 Lucas Mason-Brown What is a unipotent… November 25 Lucas Mason-Brown What is a unipotent representation	November 25 Lucas Mason-Brown What is a unipotent representation An automated programme has been used to generate the subtitles on this talk. icms repnet representations orbit unit character representation ideals orbits ideal classification equivalence coverings cover groups quantisation algebra nilpotent cheque bash definition bar From OLLIE Quinn on November 26th, 2020 0 likes 89 plays 0 Course Code ICMS Publisher ICMS Licence Type All rights reserved Language English Date Created November 26th, 2020
54:43 Desmond Higham 16 November Should We Be Perturbed… Desmond Higham 16 November Should We Be Perturbed About Deep Learning	Desmond Higham 16 November Should We Be Perturbed About Deep Learning ICMS OWNA An automated programme has been used to generate the subtitles on this talk. icms owna point error network precision attack softmax points epsilon algorithm change classification dimension errors function validation output images analysis perturbations things From OLLIE Quinn on November 16th, 2020 0 likes 262 plays 0 Course Code ICMS Publisher ICMS Licence Type All rights reserved Language English Date Created November 16th, 2020
09:48 ML5: K-Nearest Neighbours Classification ML5: K-Nearest Neighbours Classification	ML5: K-Nearest Neighbours Classification In this video, we introduce the simplest classifier: the K-Nearest Neighbours classifier. This classifier has no trainable parameters, and classifies test points by finding its closest training… test point training neighbors class points classifier validation classification parameters hyperparameters vector dog george distance spam cat jackson vote step From Elliot Crowley on November 9th, 2020 0 likes 9 plays 0
03:03 AHMED_Salahuddin AHMED_Salahuddin	AHMED_Salahuddin PLEASE NOTE: The video captions have been auto generatedStudent name: AHMED, Salahuddin Presentation title: Community use of digital auscultation to improve diagnosis of childhood pneumonia in… respire asm lung children pneumonia auscultation crackles classification stethoscope care inclusion algorithm childhood diagnosis members end providers accuracy sound machine provider sounds From Claire Jankowska on November 3rd, 2020 0 likes 37 plays 0 Licence Type All rights reserved The University of Edinburgh Language English Date Created November 3rd, 2020
04:19 MLP Lecture 07 - Clip 09 - a Short History of CNNs MLP Lecture 07 - Clip 09 - a Short History of CNNs	MLP Lecture 07 - Clip 09 - a Short History of CNNs Machine Learning Practical (MLP) Lecture 07, Clip 09 / 09. machine learning deep learning times maps feature network size image nets class performance networks kernel features gradients lecture layers probability classes error classification model From Pavlos Andreadis on November 2nd, 2020 1 likes 159 plays 0 Course Code INFR11132 Licence Type All rights reserved Language English
55:52 Homogeneous Hamiltonian operators, projective… Homogeneous Hamiltonian operators, projective geometry and integrable systems - Raffaele Vittolo	Homogeneous Hamiltonian operators, projective geometry and integrable systems - Raffaele Vittolo VISS online seminar series Homogeneous Hamiltonian operators, projective geometry and integrable systems Raffaele Vittolo (University of Salento) 21 October 2020 An automated programme is used to… mathematics geometry operator operators systems equation form transformations linear equations transformation variables respect matrix coordinates classification space component structure conditions mechanism coefficient From GILLIAN KERR on October 21st, 2020 0 likes 45 plays 0 Licence Type All rights reserved Language english Date Created October 21st, 2020
11:04 MLP Lecture 04 - Clip 05 - L1&L2… MLP Lecture 04 - Clip 05 - L1&L2 Regularisation	MLP Lecture 04 - Clip 05 - L1&L2 Regularisation Machine Learning Practical (MLP) Lecture 04, Clip 05 / 05. machine learning deep learning weights weight function regularization error gradient term decay sum images space network thing difference pizza rate training entropy classification image From Pavlos Andreadis on October 11th, 2020 1 likes 246 plays 0 Course Code INFR11132 Licence Type All rights reserved Language English
01:40:06 Net cultures 2019 Net cultures 2019	Net cultures 2019 2019 class recording including student videos internet studies knowledge things kind sorts society idea lot ideas source thing bit computer economy classification ways week concept sort age tools From James Stewart on October 1st, 2020 0 likes 6 plays 0 Publisher UoE Licence Type Creative Commons - Attribution Non Commercial No Derivatives Date Created September 24th, 2019
45:03 Conversation about CVI with Dr Cathy Williams Conversation about CVI with Dr Cathy Williams	Conversation about CVI with Dr Cathy Williams Here Cathy and I talk about assessment, assessments within special schools, research, research instruments and the need for collaboration. This conversation about CVI is really for those researchers… cvi ravenscroft international classification of functioning icf From John Ravenscroft on July 24th, 2020 0 likes 30 plays 0 Licence Type Refer to YouTube licence Language English Date Created July 24th, 2020
10:48 Supervised Learning Supervised Learning	Supervised Learning Supervised Learning hds tree decision classification training learning networks regression trees layers values neighbors variables output prediction model algorithm examples neuron node york From Dimitrios Doudesis on June 17th, 2020 0 likes 89 plays 0 Course Code GLHE11086 Licence Type Creative Commons - Attribution Share A Like
01:17:45 Promoting fairer access to Scottish universities:… Promoting fairer access to Scottish universities: how can this be achieved?	Promoting fairer access to Scottish universities: how can this be achieved? This is the second annual memorial lecture in honour of the late Professor David Raffe. Professor Vikki Bolover of Durhan University drew on her recent research in Scotland to consider how we can… speaker 2 false negatives support students degree program access threshold long time 1st year justice knowledge gaps disavantaged students institution specific academic skills social background individual level secondary school effects bottom quintile percent mark scottish funding council disadvantaged students speaker 1 association of commonwealth universities free school widening participation high grades disadvantaged backgrounds scottish academic degrees selective universities fair admission measurement subject knowledge gaps universities human communication disadvantaged young people area level measure mentioned earlier lecture egalitarianism tariffs universities universities in scotland english degree educational stages scottish context higher tariff universities deprived neighborhood audience member education in england degree classification lecture recording political science terms u.k. communication good degree scottish universities disadvantage person genuinely fair equality advantage group heroic disadvantage fair access entry requirement deprived background winding access coded red false positives david raffe admitting students 1st question s.f.c. people coming contextualised admissions higher education youth-led organizations percent target paradigm shift disadvantaged people cognitive science young people caption complete formal equality scotland free school meals scientific method procedural justice widening access edinburgh individual university academically selective entire population medium tariff universities deprived communities business terms shallow relationship fairer access From Ileana Iacobescu on April 10th, 2019 0 likes 55 plays 0 Licence Type All rights reserved The University of Edinburgh
07:22 ROC Curve ROC Curve	ROC Curve ROC Curve - Week 2 classification outcome dimensional space predictive analytics micromasters biostatistics point sensitivity prediction 100 percent false negatives confusion matrix predictive algorithm specificity threshold caption complete calculate positive percent negative false false true positives false positive rate cut offs negative observations effects high recall working dogs false positive observation zero roc curve From Calum Macphail on April 1st, 2019 0 likes 162 plays 0 Course Code PA1.2x Evaluation of Predictive Modelling Licence Type Creative Commons - Attribution Share A Like Language English

Search for tag: "classification"

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