12/08/2020 Code and Proofs (Week 2 Wed) Code and Proofs (Week 2 Wed) Fractal Trees fracTree :: Picture fracTree = tree 10 (Point 400 800) (Vector 0 (-100)) red tree :: Int -> Point -> Vector -> Colour -> Picture tree depth base direction colour | depth == 0 = drawLine line | otherwise = […]
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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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Exercise 5 GADTs TypeSafe printf More on Vectors Administrivia 1 Software System Design and Implementation GADTs Practice Curtis Millar CSE, UNSW (and Data61) 22 July 2020 Exercise 5 GADTs TypeSafe printf More on Vectors Administrivia 2 Parse a series of tokens. Stack push and pop. Evaluate a sequence of tokens. Calculate a string. Exercise 5
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
ECON3350/7350 Univariate Time Series – II Eric Eisenstat The University of Queensland Lecture 3 Eric Eisenstat (School of Economics) ECON3350/7350 Week 3 1 / 23 Estimation of Univariate Time Series Models Recommended readings Author Title Chapter Call No Enders Verbeek Applied Econometric Time Series, 4e A Guide to Modern Econometrics 2 8.7, 8.8 HB139 .E55
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Distributional Semantics COMP90042 Natural Language Processing Lecture 10 COPYRIGHT 2020, THE UNIVERSITY OF MELBOURNE 1 COMP90042 L10 • Manually constructed ‣ Expensive ‣ Human annotation can be biased and noisy • Language is dynamic ‣ New words: slang, terminology, etc. ‣ New senses • The Internet provides us with massive amounts of text. Can we
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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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