COMP3308/3608 Introduction to Artificial Intelligenece (regular and advanced) semester 1, 2020 Information about the exam The exam will be online, via Canvas, un-proctored. It is set as a Quiz. The Canvas site for the exam is different that the Canvas site we use during the semester. There are 2 exam sites: one for […]
COMP3308/3608, Lecture 8b & 9a ARTIFICIAL INTELLIGENCE Multilayer Neural Networks and Backpropagation Algorithm Reference: Russell and Norvig, pp. 731-737 Witten, Frank and Hall, pp. 232-241 Irena Koprinska, irena.koprinska@sydney.edu.au COMP3308/3608 AI, week 8b & 9a, 2021 1 • Multilayer perceptron NNs • XOR problem • neuron model • Backpropagation algorithm • Derivation • Example • Universality
COMP3308/3608 Artificial Intelligence Week 8 Tutorial exercises Perceptrons. Multilayer Neural Networks 1. Exercise 1. Perceptron learning (Homework) The homework for this week consists of 5 multiple-choice questions with two possible answers – True and False. To complete the homework, please go to Canvas -> Quizzes->w8-homework-submission. Remember to press the “Submit” button at the end. Only
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COMP3308/3608 Artificial Intelligence Week 9 Tutorial exercises Multilayer Neural Networks 2. Deep Learning. Exercise 1. Backpropagation (Homework) a) Show that the derivative of the hyperbolic tangent sigmoid is 1-a2. b) Write the weight change rule for this function, using the result from a). See slide 26 from the lecture on backpropagation. for an output neuron
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COMP3308/COMP3608, Lecture 1 ARTIFICIAL INTELLIGENCE Introduction to Artificial Intelligence Irena Koprinska Reference: Russell and Norvig, ch. 1 [ch. 2, ch. 26 – optional] Irena Koprinska, irena.koprinska@sydney.edu.au COMP3308/3608 AI, week 1, 2021 1 Outline • Administrative matters • Course overview • What is AI? • A brief history • The state of the art Irena Koprinska,
COMP3308/3608, Lecture 9b ARTIFICIAL INTELLIGENCE Deep Learning Tutorials on Deep Learning: 1) http://cs.stanford.edu/~quocle/tutorial1.pdf 2) http://cs.stanford.edu/~quocle/tutorial2.pdf 3) http://deeplearning.stanford.edu/tutorial/ Irena Koprinska, irena.koprinska@sydney.edu.au COMP3308/3608 AI, week 9b, 2021 1 Outline • What is deep learning? • Autoencoder neural networks • Convolutional neural networks • Applications Irena Koprinska, irena.koprinska@sydney.edu.au COMP3308/3608 AI, week 9b, 2021 2 What is Deep Learning?
ECE 533 Final Exam Assigned: Monday, April 30th, 2018. Due: Tuesday, May 1st, 2018. You must turn it in online or under my door No collaboration is allowed. You must direct all questions to the instructor ONLY. All problems are weighted equally. Problem #1 (Sampling a continuous function). Please note that this problem essen- tially
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Springer Texts in Statistics Series Editors: G. Casella S. Fienberg I. Olkin For further volumes: http://www.springer.com/series/417 Gareth James • Daniela Witten • Trevor Hastie Robert Tibshirani An Introduction to Statistical Learning with Applications in R 123 Gareth James Department of Information and Operations Management University of Southern California Los Angeles, CA, USA Trevor Hastie Department
COMP90051 Statistical Machine Learning Project 2 Specification Due date: 5pm Tuesday 18t h October 2022 (competition closes 12pm noon) Melbourne timezone Weight: 25% Competition link: https://www.kaggle.com/t/90c01d1a83664fd3b57e1889c2b2ad44 1 Overview Authorship attribution is the task of identifying the author of a given document. It has been studied extensively in machine learning, natural language processing, linguistics, and privacy
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Assessment Task 2 Stochastic Optimization using Population Based Incremental Learning A/Prof Stuart Perry School of Electrical and Data Engineering, University of Technology Sydney UTS CRICOS 00099F What we will cover 1. Introduction 2. What you have to do 3. How you submit your work 41082 Introduction to Data Engineering Where we are at in the
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