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04:54 Spirion - Run Scan Spirion - Run Scan	Spirion - Run Scan To Run your Spirion sensitive data scan:- Open Spirion and login- Open "Advanced Interface"- Set data types- Set locations -> be sure to include EMAIL, and under location options select… spirion identity finder idf sensitive data confidential data scan match files function options locations location types start redact resources cit things screen summary programs return interface password menu From Christi Smith 2 plays Creation Date May 16, 2022 Creator 1 First Name Christi Creator 1 Last Name Smith Creator 1 NetID cns64 Language English Searchable Yes Searchable On Cornell Web Search
49:32 ANSC 403: Lecture 4- Plant & Animal Mineral… ANSC 403: Lecture 4- Plant & Animal Mineral Requirements Toxicity & Deficiencies, Unit 3: Tropical Forages & Browses. Morfological Features	ANSC 403: Lecture 4- Plant & Animal Mineral Requirements Toxicity & Deficiencies, Unit 3: Tropical Forages & Browses. Morfological Features plants plant silicon elements animal animals toxicity selenium thing deficiency plan function things element leaves problem food grass table nickel From Kelly Heath 0 plays Creation Date February 03, 1994 Searchable Unlisted
01:07:07 12.6.21 Francesca Parise, Cornell University 12.6.21 Francesca Parise, Cornell University	12.6.21 Francesca Parise, Cornell University Zoom Recording ID: 97325240794 UUID: VR9rzlliTaWlGPlYA/SSuA== Meeting Time: 2021-12-06T20:28:26Z network function graph equilibrium agent agents problem intervention bit population kind game question probability thing idea model parameter fact dynamics From Jordan Staiti 12 plays
59:34 12.3.21 Andrew Bennett, Cornell University 12.3.21 Andrew Bennett, Cornell University	12.3.21 Andrew Bennett, Cornell University Zoom Recording ID: 471873857 UUID: 9k4LfuojS7u54l2B6EhMRw== Meeting Time: 2021-12-03T16:59:15Z kind problem function reward evaluation setting action functions outcome methods sir things model lot method observation distribution actions compounding result From Jordan Staiti 11 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 51 plays
01:21:51 Lecture 11/16/21: Generalized low rank models Lecture 11/16/21: Generalized low rank models	Lecture 11/16/21: Generalized low rank models Zoom Recording ID: 94347618999 UUID: SAWHob9hQW2cn6V0EZ64cg== Meeting Time: 2021-11-16T14:36:06Z loss values model entries function rank regularization sum vector matrix times points error thing columns w. column norm dataset kind From Madeleine Udell 75 plays
01:11:40 11.15.21 Rad Niazadeh, University of Chicago 11.15.21 Rad Niazadeh, University of Chicago	11.15.21 Rad Niazadeh, University of Chicago Zoom Recording ID: 97325240794 UUID: lYgN9LNUQc6At0ybS4kIUw== Meeting Time: 2021-11-15T20:30:01Z function algorithm point path points strategy coordinate problem lambda question fact condition paper approximation thing change game half extension inequality From Jordan Staiti 5 plays
01:21:07 Lecture 11/11: Imputing missing values with the… Lecture 11/11: Imputing missing values with the Gaussian copula	Lecture 11/11: Imputing missing values with the Gaussian copula Zoom Recording ID: 94347618999 UUID: me8ufsMXTCm5sdTAuyJ8PQ== Meeting Time: 2021-11-11T14:31:38Z model distribution variables imputation datasets question variable function questions features points assumption invitation values thing estimation observation hyperparameters parameter column From Madeleine Udell 70 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 132 plays
56:52 11.8.21 Jonathan Shafer, University of California… 11.8.21 Jonathan Shafer, University of California Berkeley	11.8.21 Jonathan Shafer, University of California Berkeley Zoom Recording ID: 97325240794 UUID: WdibhqUfR3CJI3DNJ4LmfA== Meeting Time: 2021-11-08T20:30:31Z learning algorithm function question gap class thing hypothesis bit tree lot sample dimension epsilon probability prover label output machine point From Jordan Staiti 10 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 131 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 131 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 10 plays
01:23:23 Lecture 10/21/21: Ensembles + Regularization Lecture 10/21/21: Ensembles + Regularization	Lecture 10/21/21: Ensembles + Regularization Zoom Recording ID: 94347618999 UUID: Hg/UOyaZTFGi+ub+trRDtw== Meeting Time: 2021-10-21T13:31:18Z tree trees prediction model variance models loss thing gradient bootstrap samples predictions dataset regularization error learners idea function training values From Madeleine Udell 73 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 100 plays
42:40 Section 10/13/21: Advanced scikit-learn Section 10/13/21: Advanced scikit-learn	Section 10/13/21: Advanced scikit-learn Zoom Recording ID: 95071859677 UUID: T29yk1x8Sr6W6XqR4hrTMQ== Meeting Time: 2021-10-13T15:19:21Z pipeline things column values hyperparameters model scikit columns bunch encoding kind lot test steps step function text hyperparameter idf guys From Madeleine Udell 33 plays