On Beyond Minimax and Games AIMA 5.3 – 5.5 CMPSC 442 Week 5, Meeting 13, Three Segments Outline ● Heuristic Alpha-Beta, and Other Hybrid Search ● Monte Carlo Tree Search ● Stochastic Games: Expectiminimax ● Other Variants, and Limitations Outline, Wk 5, Mtg 13 2 On Beyond Minimax and Games AIMA 5.3 – 5.5 CMPSC […]
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CMPSC442-Wk12-Mtg35 Introduction to Deep Learning and Neural Networks AIMA 21.1 – 21.6 CMPSC 442 Week 12, Meeting 35, Three Segments Outline ● Intro to Deep Learning and Neural Networks ● Computation Graphs ● Convolutional Networks versus Recurrent Networks 2Outline, Wk 12, Mtg 35 CMPSC 442 Week 12, Meeting 35, Segment 1 of 3: Intro to
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Natural Language Processing CMPSC 442 Week 13, Meeting 38, Three Segments Outline ● Early Decades ● Shift to Machine Learning Paradigm ● NLP Deep Learning: Excerpts from Mirella Lapata 2017 Keynote 2Outline, Wk 13, Mtg 37 Natural Language Processing CMPSC 442 Week 13, Meeting 38, Segment 1: Early Decades Early Vision ● The Ultimate Goal
PowerPoint Presentation Week 10: Adversarial Machine Learning – Vulnerabilities (Part II) Explanation, Detection & Defence COMP90073 Security Analytics Yi Han, CIS Semester 2, 2021 COMP90073 Security Analysis Overview • Adversarial machine learning beyond computer vision – Audio – Natural language processing (NLP) – Malware detection • Why are machine learning models vulnerable? – Insufficient training
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Microsoft Word – FinalExamStudyGuidesFall2021.docx DS/CMPSC 410 Programming Models for Big Data Fall 2021 Final Exam Study Guide December 6, 2021 The weight of each topic is an estimate. The actual weight of exam questions can vary slightly. A question in the exam can also related to more than one topic areas. 1. Big Data Opportunities,
Natural Language Processing Jacob Eisenstein October 15, 2018 Contents Contents 1 Preface i Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i How to use
Machine learning lecture slides Machine learning lecture slides COMS 4771 Fall 2020 0 / 36 Optimization II: Neural networks Outline I Architecture of (layered) feedforward neural networks I Universal approximation I Backpropagation I Practical issues 1 / 36 Parametric featurizations I So far: data features (x or ϕ(x)) are fixed during training I Consider a
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l21-summarisation-v4 COPYRIGHT 2021, THE UNIVERSITY OF MELBOURNE 1 COMP90042 Natural Language Processing Lecture 21 Semester 1 2021 Week 11 Jey Han Lau Summarisation COMP90042 L21 2 Summarisation • Distill the most important information from a text to produce shortened or abridged version • Examples ‣ outlines of a document ‣ abstracts of a scientific article
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01-preprocessing Preprocessing with NLTK¶ First, if you haven’t used iPython notebooks before, in order to run the code on this workbook, you can use the run commands in the Cell menu, or do shift-enter when an individual code cell is selected. Generally, you will have to run the cells in order for them to work
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02-bpe Train BPE on a toy text example bpe algorithm: https://web.stanford.edu/~jurafsky/slp3/2.pdf (2.4.3) In [ ]: import re, collections text = “The aims for this subject is for students to develop an understanding of the main algorithms used in naturallanguage processing, for use in a diverse range of applications including text classification, machine translation, and question answering. Topics
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