ECON3350/7350 Volatility Models – I Eric Eisenstat The University of Queensland Lecture 7 Eric Eisenstat (School of Economics) ECON3350/7350 Week 8 1 / 24 Volatility Models Recommended readings Author Title Chapter Call No Enders Verbeek Applied Econometric Time Series, 4e A Guide to Modern Econometrics 3 8.11 HB139 .E55 2015 HB139 .V465 2012 Eric Eisenstat […]
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Lecture 7 (part 2): Logistic Regression COMP90049 Introduction to Machine Learning Semester 1, 2020 Lea Frermann, CIS 1 Roadmap Sofar… • Naive Bayes • Optimization (closed-form and iterative) • Evaluation Today : more classification! • Logistic Regression 2 Logistic Regression Quick Refresher Recall Naive Bayes P(x, y) = P(y)P(x|y) = P(yi ) P(xmi
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Compilers and computer architecture: Caches and caching Martin Berger 1 December 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 Caches in modern CPUs Today we will learn about caches in modern CPUs. They are crucial for high-performance programs and high-performance compilation. Today’s material can safely be ignored
Computational Linguistics CSC 485 Summer 2020 5 5. Chart parsing Gerald Penn Department of Computer Science, University of Toronto Reading: Jurafsky & Martin: 13.3–4. Allen: 3.4, 3.6. Bird et al: 8.4, online extras 8.2 to end of section “Chart Parsing in NLTK”. Copyright © 2017 Suzanne Stevenson , Graeme Hirst and Gerald Penn. All rights
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Exercise 1 Property Based Testing Proofs and Tests Homework Consultations 1 Software System Design and Implementation Property Based Testing Practice Curtis Millar CSE, UNSW (and Data61) 17 June 2020 Exercise 1 Property Based Testing Proofs and Tests Homework Consultations 2 1 2 3 Simple Picture: add the chimney and smoke Moving Objects: implement movePictureObject Generating
13 practice problems. 1. You are given a text T of n characters, and your goal is to find the length of a subsequence in T that is as long as possible and has the property that it reads the same forward and backward. For example, in XABCBBACXA, we can find XAB…BBA…X… to form the
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NEW SOUTH WALES Algorithms: COMP3121/9101 School of Computer Science and Engineering University of New South Wales 6. THE GREEDY METHOD COMP3121/3821/9101/9801 1 / 46 The Greedy Method Activity selection problem. Instance: A list of activities ai, (1 ≤ i ≤ n) with starting times si and finishing times fi. No two activities can take place
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NEW SOUTH WALES Algorithms: COMP3121/9101 School of Computer Science and Engineering University of New South Wales 7. DYNAMIC PROGRAMMING COMP3121/3821/9101/9801 1 / 40 Dynamic Programming The main idea of Dynamic Programming: build an optimal solution to the problem from optimal solutions for (carefully chosen) smaller size subproblems. Subproblems are chosen in a way which allows
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ECON 3350/7350: Applied Econometrics for Macroeconomics and Finance Tutorial 6: Cointegration In this tutorial you will test for cointegration using the Engle-Granger method. The data you use are a system of four Australian interest rates: the 5 year (i5y) and 3 year (i3y) Treasury Bond (Capital Market) rates, and the 180 day (i180d) and 90
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Data Invariants and ADTs Validation Data Refinement Administrivia 1 Software System Design and Implementation Data Invariants, Abstraction and Refinement Liam O’Connor University of Edinburgh LFCS (and UNSW) Term 2 2020 Data Invariants and ADTs Validation Data Refinement Administrivia 2 Motivation We’ve already seen how to prove and test correctness properties of our programs. How do
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