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 […]
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
60-256 System Programming: Process Control III Content COMP 2560 System Programming: Process Control III Courtesy of Dr. B. Boufama modified by Dan Wu School of Computer Science University of Windsor – Instructor: Dr. Dan Wu Process Control III 1 Copyright @ 2019, 2020, 2021 all rights reserved Content Content 1 Differentiating a process: exec() 2
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Kernel Methods COMP9417 Machine Learning and Data Mining Term 2, 2021 COMP9417 ML & DM Kernel Methods Term 2, 2021 1 / 47 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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Unsupervised Learning COMP9417 Machine Learning and Data Mining Term 2, 2021 COMP9417 ML & DM Unsupervised Learning Term 2, 2021 1 / 76 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
COMP9417: A collection of sample exam exercises August 5, 2021 Note to student: Some of these questions are longer/more difficult than the questions that will be on the actual final exam. With that being said, you should aim to work through and understand all questions here as part of your revision. Note that this is
Lecture 10 Computer Memory (II): Internal and External Memory Recap from Last Lecture ◆Memory hierarchy: Copyright By PowCoder代写 加微信 powcoder – Use a hierarchical system of memories to emulate an idea memory (fast, large, and relatively cheap) – Why is it possible? – Locality of Reference – Example: cache and main memory Recap from Last
Probabilistic modelling How could have this data been generated? Can you fit models to the data? Copyright By PowCoder代写 加微信 powcoder Gaussian mixture models (GMMs) § Assume data was generated by a set of Gaussian distributions. § The probability density is a mixture of them. § Find the parameters of the Gaussian distributions and how
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COMP3308/3608, Lecture 7 ARTIFICIAL INTELLIGENCE Decision Trees Reference: Witten, Frank, Hall and Hall: ch.4.3 and ch.6.1 Russell and Norvig: p.697-707 Copyright By PowCoder代写 加微信 powcoder , COMP3308/3608 AI, week 7, 2022 1 Core topics: • Constructing decision trees • Entropy and information gain • DT’s decision boundary Additional topics: • Avoiding overfitting by pruning •
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1 Notation COMP3308/COMP3608 Back Propagation of a Multi-layer Neural Network This is the notation that will be used for this example: Copyright By PowCoder代写 加微信 powcoder 1 … i neti Output Neurons Hidden Neurons Input Neurons Given by the example: – input neuron outputs (ok) – all initial edge weights (wkj, wji) – all initial
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