NEW SOUTH WALES Algorithms: COMP3121/3821/9101/9801 School of Computer Science and Engineering University of New South Wales Sydney 2. DIVIDE-AND-CONQUER COMP3121/3821/9101/9801 1 / 28 A Puzzle An old puzzle: We are given 27 coins of the same denomination; we know that one of them is counterfeit and that it is lighter than the others. Find the […]
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Compilers and computer architecture: Realistic code generation Martin Berger 1 November 2019 1Email: M.F.Berger@sussex.ac.uk, Office hours: Wed 12-13 in Chi-2R312 1/1 Recall the function of compilers 2/1 Recall the structure of compilers Source program Lexical analysis Intermediate code generation Optimisation Syntax analysis Semantic analysis, e.g. type checking Code generation Translated program 3/1 Introduction We have
Computational Linguistics CSC 485 Summer 2020 3 Reading: Jurafsky & Martin: 19.1–4, 20.8; Bird et al: 2.5 Copyright © 2017 Graeme Hirst, Suzanne Stevenson and Gerald Penn. All rights reserved. 3. Lexical semantics Gerald Penn Department of Computer Science, University of Toronto Lexical semantics • Word meanings and their internal structure. • The structure of
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Question 1 is on Linear Regression and requires you to refer to the following training data: xy 42 64 12 10 25 23 29 28 46 44 59 60 We wish to fit a linear regression model to this data, i.e. a model of the form: yˆ i = w 0 + w 1 x
Tutorial Week 7 Task 1. Develop type-checking algorithms for the following language constructs, along the lines of the type-checking algorithm we developed in the lectures. • P/Q • while0 P do Q. This is like conventional while except that the loop terminates when P hits 0. • repeat P until Q • letrec x :
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Workshop 3 COMP90051 Natural Language Processing Semester 1, 2020 COMP90051 Natural Language Processing (S1 2020) Workshop 3 Jun Wang • Online lectures and tutorials • Recording • Questions COMP90051 Natural Language Processing (S1 2020) Workshop 3 Jun Wang Materials • Download files • Workshop-03.pdf • 03-classification.ipynb • 04-ngram.ipynb • From Canvas – Modules – Workshops
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Classification (1) COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Classification (1) Term 2, 2020 1 / 72 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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Lecture 9: Neural Networks COMP90049 Introduction to Machine Learning Semester 1, 2020 Lea Frermann, CIS 1 Roadmap So far … Classification and Evaluation • Naive Bayes, Logistic Regression, Perceptron • Probabilistic models • Loss functions, and estimation • Evaluation 2 Roadmap So far … Classification and Evaluation • Naive Bayes, Logistic Regression, Perceptron • Probabilistic
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Compilers and computer architecture From strings to ASTs (2): context free grammars Martin Berger 1 October 2019 1Email: M.F.Berger@sussex.ac.uk, Office hours: Wed 12-13 in Chi-2R312 1/1 Recall the function of compilers 2/1 Recall we are discussing parsing Source program Lexical analysis Intermediate code generation Optimisation Syntax analysis Semantic analysis, e.g. type checking Code generation Translated
Part of speech tagging COMP90042 Natural Language Processing Lecture 5 COPYRIGHT 2020, THE UNIVERSITY OF MELBOURNE 1 COMP90042 L5 • • • 2 assignments (down from 3) 20% of subject (no change) 1st assignment will be released in week 4 Assignments 2 COMP90042 L5 • • Online workshops available till week 12 Workshop slides by
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