Learning Parameters of Multi-layer Perceptrons with Backpropagation COMP90049 Introduction to Machine Learning Semester 1, 2020 Lea Frermann, CIS 1 Roadmap Last lecture • From perceptrons to neural networks • multilayer perceptron • some examples • features and limitations Today • Learning parameters of neural networks • The Backpropagation algorithm 2 Recap: Multi-layer perceptrons 1 x1 […]
NEW SOUTH WALES Algorithms COMP3121/9101 School of Computer Science and Engineering University of New South Wales 4. FAST LARGE INTEGER MULTIPLICATION – part A COMP3121/9101 1 / 35 Basics revisited: how do we multiply two numbers? The primary school algorithm: X X X X
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Artificial Intelligence Review What is an Agent? An entity □ situated: operates in a dynamically changing environment □ reactive: responds to changes in a timely manner □ autonomous:cancontrolitsownbehaviour □ proactive:exhibitsgoal-orientedbehaviour □ communicating: coordinate with other agents?? Examples: humans, dogs, …, insects, sea creatures, …, thermostats? Where do current robots sit on the scale? Lectures Environment
Learning Theory COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Learning Theory Term 2, 2020 1 / 78 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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Learning Theory COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Learning Theory Term 2, 2020 1 / 78 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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COMP251: Greedy algorithms Jérôme Waldispühl School of Computer Science McGill University Based on (Cormen et al., 2002) Based on slides from D. Plaisted (UNC) & (goodrich & Tamassia, 2009) Overview • Algorithm design technique to solve optimization problems. • Problems exhibit optimal substructure. • Idea (the greedy choice): – When we have a choice to
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Lecture VI Extensions to Complex Matrices, in particular Hermitian Matrices. Key Notions: * Unitary matrices * Unitary equivalence * Schur’s unitary triangularization * QR factorization * Congruence and simultaneous diagonalization Fall 2020 Prof.Jiang@ECE NYU 249 Fall 2020 Prof.Jiang@ECE NYU 250 Orthogonality Between Complex Vectors Given any pair of (complex) vectors x, y n ,
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Back to the motivating problem: Fall 2020 Prof.Jiang@ECE NYU 63 Lecture II Solving m equations for n unknowns: a x a x b 111 1nn1 a x a x b 211 2nn2 Ax b. a x a x b m11 mnnm When does a solution exist? When unique? Both n
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• Extension and Applications Lecture V • The higher dimensional case of general real symmetric matrices Fall 2020 Prof.Jiang@ECE NYU 210 to the diagonal form: Fall 2020 k Prof.Jiang@ECE NYU 211 The General Case We already proved the result with N 2. By induction, let us assume that for each integer 1 k
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Automata, Computability and Complexity: Theory and Applications Elaine Rich Originally published in 2007 by Pearson Education, Inc. © Elaine Rich With minor revisions, July, 2019. Table of Contents PREFACE ………………………………………………………………………………………………………………………………..VIII ACKNOWLEDGEMENTS…………………………………………………………………………………………………………….XI CREDITS…………………………………………………………………………………………………………………………………..XII PARTI: INTRODUCTION…………………………………………………………………………………………………………….1 1 2 3 4 Why Study the Theory of Computation? ……………………………………………………………………………………………2 1.1 The Shelf Life of Programming Tools ………………………………………………………………………………………………2 1.2 Applications