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57:40 Stochastic Networks Conference 2022 - Neil Walton… Stochastic Networks Conference 2022 - Neil Walton Edit	Stochastic Networks Conference 2022 - Neil Walton Edit Zoom Recording ID: 97630276119 UUID: U0km4eAFT/qJV3mJGOGpqw== Meeting Time: 2022-06-22 12:35:35pm kind point things function algorithm probability arm sort thing problem target rewards arms distribution learning queue linear boundary lyapunov result From E. Cornelius 0 plays
54:24 Stochastic Networks Conference 2022 - Mengdi Wang… Stochastic Networks Conference 2022 - Mengdi Wang Edit	Stochastic Networks Conference 2022 - Mengdi Wang Edit Zoom Recording ID: 97630276119 UUID: /ZuEXbU4RMCsfkAf8RJ+Zw== Meeting Time: 2022-06-24 12:36:58pm sequence recombination optimization mutation problem model evolution sequences process function selection question population lab linear protein learning theory algorithm bit From E. Cornelius 1 plays
01:00:56 Clip of Stochastic Networks Conference 2022 -… Clip of Stochastic Networks Conference 2022 - Christina Lee Yu Edit	Clip of Stochastic Networks Conference 2022 - Christina Lee Yu Edit Zoom Recording ID: 97630276119 UUID: /ZuEXbU4RMCsfkAf8RJ+Zw== Meeting Time: 2022-06-24 12:36:58pm treatment estimator degree model outcomes effect neighborhood individuals linear network outcome kind thing vector control graph individual function edges experiment From E. Cornelius 7 plays
01:28:26 Lecture 12/7: Review Lecture 12/7: Review	Lecture 12/7: Review Zoom Recording ID: 94347618999 UUID: VheGqdJfRNW1xraslG/csw== Meeting Time: 2021-12-07T14:31:46Z model models features sort machine class things learning lots questions lot problems feature problem linear decision methods ways gradient loss From Madeleine Udell 36 plays
01:38:47 Lecture 12/2/21: Neural networks Lecture 12/2/21: Neural networks	Lecture 12/2/21: Neural networks Zoom Recording ID: 94347618999 UUID: 911ow0ynTaqsKu+54L3rUg== Meeting Time: 2021-12-02T14:31:48Z function gradient layer linear thing networks network lot respect differentiation output matrix model kind bit nets lots models times activation From Madeleine Udell 62 plays
01:20:01 Lecture 11/18/21: Automated machine learning Lecture 11/18/21: Automated machine learning	Lecture 11/18/21: Automated machine learning Zoom Recording ID: 94347618999 UUID: swJzf2foSJGgxZQh1SLErA== Meeting Time: 2021-11-18T14:37:07Z model models machine dataset learning datasets hyperparameters lot hyperparameter feature vector error points classifier regression cross values linear features pca From Madeleine Udell 83 plays
01:42:26 Lecture 11/9/21: Unsupervised learning (PCA) Lecture 11/9/21: Unsupervised learning (PCA)	Lecture 11/9/21: Unsupervised learning (PCA) Zoom Recording ID: 94347618999 UUID: i0nonjabSNqNawDsvIrVAQ== Meeting Time: 2021-11-09T14:28:43Z loss times entries problem matrix function rank column pca model vector entry thing linear feature values regression columns features sum From Madeleine Udell 146 plays
01:30:32 Lecture 11/4/21: Losses for classification and… Lecture 11/4/21: Losses for classification and multi-class classification	Lecture 11/4/21: Losses for classification and multi-class classification Zoom Recording ID: 94347618999 UUID: l1RpSW+qTF6f/eH+Nbq9vQ== Meeting Time: 2021-11-04T13:33:44Z loss function problem class classification thing prediction margin space probability vector output linear points bit models kind regression things numbers From Madeleine Udell 141 plays
45:40 Section: post-hoc interpretability (LIME, SHAP) Section: post-hoc interpretability (LIME, SHAP)	Section: post-hoc interpretability (LIME, SHAP) Zoom Recording ID: 95071859677 UUID: 8jQJz7E4RLS27UeD6NpZ6w== Meeting Time: 2021-11-03T15:17:11Z model decision machine prediction models kind feature box class performance linear sort points instance bunch algorithm things learning features guys From Madeleine Udell 16 plays
01:51:18 Lecture 11/2/21: Loss functions Lecture 11/2/21: Loss functions	Lecture 11/2/21: Loss functions Zoom Recording ID: 94347618999 UUID: vWQG3ObBTcWaFwtVIF8PgQ== Meeting Time: 2021-11-02T13:05:02Z loss regression outliers points function error times distance squares prediction point problem linear alpha errors quantile model sum functions questions From Madeleine Udell 144 plays
44:41 CTBN October 2021 Forum CTBN October 2021 Forum	CTBN October 2021 Forum Enhancing Competitiveness via Embedded Digitization -How Digital Apps and AI Speed Up B2B Lending and Transactions “You’re Approved!” is music to one business… engineering engineering alumni engineering alumni association ceaa ctbn cornell technology business network digital digital apps b2b lending transactions economy lot kind things linear credit cornell question fraud banks customer point bank process bit businesses banking companies opportunity customers From Bonna Davidson 12 plays
57:14 Section 11/23/21: optimization Section 11/23/21: optimization	Section 11/23/21: optimization Zoom Recording ID: 91739230861 UUID: v8lG4ne3RLytBr+lTovY4g== Meeting Time: 2021-10-27T15:17:37Z function expectation convex gradient star solution minus times term convergence functions definition method point k. step summation key problem linear From Madeleine Udell 15 plays
01:30:06 Lecture 10/19/21: Trees Lecture 10/19/21: Trees	Lecture 10/19/21: Trees Zoom Recording ID: 94347618999 UUID: aVtz9UcFQEWYfZSJRdBJJw== Meeting Time: 2021-10-19T13:35:26Z points region tree node decision point leaf model label split class trees linear values threshold coordinate thing percent thresholds nodes From Madeleine Udell 120 plays
01:26:58 Lecture 10/14: Bootstrap and bias variance… Lecture 10/14: Bootstrap and bias variance tradeoff	Lecture 10/14: Bootstrap and bias variance tradeoff Zoom Recording ID: 94347618999 UUID: qgdVNfgoTIi+Z1Xj3EkLqQ== Meeting Time: 2021-10-14T13:29:44Z model dataset function distribution variance points prediction bootstrap point sample models datasets expectation sigma thing linear hm times bias epsilon From Madeleine Udell 127 plays
01:07:39 ORIE Colloquium on 10/45/2021: Jeff Linderoth ORIE Colloquium on 10/45/2021: Jeff Linderoth	ORIE Colloquium on 10/45/2021: Jeff Linderoth Subspace Clustering with Missing Data via Integer Programming In the Subspace Clustering with Missing Data (SCMD) problem, we are given a collection of n data points, arranged into columns of a… subspace points problem matrix point subspaces programming linear method clustering things bit methods integer sort machine sense rank dimension completion From E. Cornelius 2 plays
40:18 Section 10/5/21: Generalization Section 10/5/21: Generalization	Section 10/5/21: Generalization Zoom Recording ID: 95071859677 UUID: TnmTC3VVQaKz+eKl3dxvxA== Meeting Time: 2021-10-05T18:41:27Z model regression validation training cross models test concept train linear sample california generalization size batch michigan point tree decision sense From Madeleine Udell 36 plays