Time: Place: Teachers: Allowed material: Not allowed: Here ζ(μ) takes values 1 or −1. i RE-EXAM for ARTIFICIAL NEURAL NETWORKS January 20, 2018, at 830 − 1230 SB Multisal Bernhard Meh Mathematics Handbook for Science and Engineering Any other written material, calculator Maximum score on this exam: 12 points. Maximum score for homework problems: 12 […]
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1 Solutions for Artifical Neural Networks September 21, 2020 2 Chapter 2 2.3 Cross-talk term (a) Apply x (ν) to the network: S = sgn N w x (ν) . For x (ν) to remain unchanged i j=1 ij j underthisupdatewemustrequirethatS =x(ν).Inotherwords ii This condition evaluates to: N p1p x(ν)=sgn w x(ν) =sgn x(μ)x(μ)
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Classification (2) COMP9417 Machine Learning and Data Mining Term 2, 2021 COMP9417 ML & DM Classification (2) Term 2, 2021 1 / 71 Acknowledgements Material derived from slides for the book “Elements of Statistical Learning (2nd Ed.)” by T. Hastie, R. Tibshirani & J. Friedman. Springer (2009) http://statweb.stanford.edu/~tibs/ElemStatLearn/ Material derived from slides for the book
Tree Learning COMP9417 Machine Learning and Data Mining Term 2, 2021 COMP9417 ML & DM Tree Learning Term 2, 2021 1 / 67 Acknowledgements Material derived from slides for the book “Machine Learning” by T. Mitchell McGraw-Hill (1997) http://www-2.cs.cmu.edu/~tom/mlbook.html Material derived from slides by Andrew W. Moore http:www.cs.cmu.edu/~awm/tutorials Material derived from slides by Eibe Frank
Recap: Logic Typed Lambda Calculus Algebraic Type Isomorphism Polymorphism and Parametricity Wrap-up 1 Software System Design and Implementation Theory of Types Christine Rizkallah UNSW Sydney Term 2 2021 Recap: Logic Typed Lambda Calculus Algebraic Type Isomorphism Polymorphism and Parametricity Wrap-up Natural Deduction Logic 2 We can specify a logical system as a deductive system by
Classification (1) COMP9417 Machine Learning and Data Mining Term 2, 2021 COMP9417 ML & DM Classification (1) Term 2, 2021 1 / 86 Acknowledgements Material derived from slides for the book “Elements of Statistical Learning (2nd Ed.)” by T. Hastie, R. Tibshirani & J. Friedman. Springer (2009) http://statweb.stanford.edu/~tibs/ElemStatLearn/ Material derived from slides for the book
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Recap: Logic Typed Lambda Calculus Algebraic Type Isomorphism Polymorphism and Parametricity Wrap-up 1 Software System Design and Implementation Theory of Types Christine Rizkallah UNSW Sydney Term 2 2021 Recap: Logic Typed Lambda Calculus Algebraic Type Isomorphism Polymorphism and Parametricity Wrap-up Natural Deduction Logic 2 We can specify a logical system as a deductive system by
Static Assurance Phantom Types GADTs Type Families 1 Software System Design and Implementation Static Assurance with Types Christine Rizkallah UNSW Sydney Term 2 2021 Static Assurance Phantom Types GADTs Type Families Methods of Assurance Static Hybrid Dynamic Testing Static Assurance Phantom Types GADTs Type Families Methods of Assurance assert() Static Hybrid Dynamic Testing Static Assurance
Property Based Testing Example Coverage Lazy Evaluation Homework 1 Software System Design and Implementation Property Based Testing; Lazy Evaluation Dr. Christine Rizkallah UNSW Sydney Term 2 2021 2 Property Based Testing Example Coverage Lazy Evaluation Homework Free Properties Haskell already ensures certain properties automatically with its language design and type system. 1 Memory is accessed
Ensemble Learning COMP9417 Machine Learning and Data Mining Term 2, 2021 COMP9417 ML & DM Ensemble Learning Term 2, 2021 1 / 51 Acknowledgements Material derived from slides for the book “Elements of Statistical Learning (2nd Ed.)” by T. Hastie, R. Tibshirani & J. Friedman. Springer (2009) http://statweb.stanford.edu/~tibs/ElemStatLearn/ Material derived from slides for the book