# D3 API Reference D3 is a [collection of modules](https://github.com/d3) that are designed to work together; you can use the modules independently, or you can use them together as part of the default build. The source and documentation for each module is available in its repository. Follow the links below to learn more. For changes […]
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The Spartan HPC System at the University of Melbourne COMP90024 Cluster and Cloud Computing University of Melbourne, March 22, 2018 lev.lafayette@unimelb.edu.au Outline of Lecture “This is an advanced course but we get mixed bag: students that have 5+ years of MPI programming on supercomputers, to students that have only done Java on Windows.” Some
FIT1047 S2 2018 Assignment 1 Submission guidelines This is an individual assignment, group work is not permitted. We use tools to check for plagiarism and collusion. Deadline: September 7th, 2018, 11:55pm Submission format: PDF for the written tasks, LogiSim circuit files for task 1, MARIE assembly files for task 2. All files must be uploaded
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Programming Language Pragmatics Programming Language Pragmatics FOURTH EDITION This page intentionally left blank Programming Language Pragmatics FOURTH EDITION Michael L. Scott Department of Computer Science University of Rochester AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Morgan Kaufmann is
Predictive Analytics – Week 11: Time Series Forecasting Predictive Analytics Week 11: Time Series Forecasting Semester 2, 2018 Discipline of Business Analytics, The University of Sydney Business School QBUS2820 content structure 1. Statistical and Machine Learning foundations and applications. 2. Advanced regression methods. 3. Classification methods. 4. Time series forecasting. 2/48 Week 11: Time Series
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Introduction to Design Optimization Formulating and analyzing a problem 1 ME 564/SYS 564 Wed Sep 5, 2018 Steven Hoffenson Goal of Week 2: To learn some strategies to analyze and reduce an optimization formulation prior to solving Recap: Class expectations • Respect from everyone toward everyone • Collaboration, learning, and helping each other • Good
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Predictive Analytics – Week 1: Introduction to Predictive Modelling Predictive Analytics Week 1: Introduction to Predictive Modelling Semester 2, 2018 Discipline of Business Analytics, The University of Sydney Business School Week 1: Introduction to Predictive Modelling 1. Content structure 2. Introduction 3. Business examples and data 4. Notation 5. Statistical decision theory 6. Evaluating model
COMP90038 Algorithms and Complexity COMP90038 Algorithms and Complexity Lecture 22: NP Problems and Approximation Algorithms (with thanks to Harald Søndergaard & Michael Kirley) Andres Munoz-Acosta munoz.m@unimelb.edu.au Peter Hall Building G.83 mailto:munoz.m@unimelb.edu.au Recap • We continued discussing greedy algorithms: • A problem solving strategy that takes the locally best choice among all feasible ones. Such choice
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ANLY-601 Spring 2018 Assignment 7 — Revised Again for Clarity, a Slight Modification to Prob. 3, and Consistency of Notation in Prob. 4 Due May 7, 2018 You may use the text, your lecture notes and handouts, and any mathematics references that you need. You may ask me for clarification but you may not consult
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Introduction to AI -Search- Introduction to AI -Planning- Francesca Toni Outline • Classical planning • Planning as search • Planning algorithms: graph-plan Recommended reading: (most of) Chapter 10 Additional reading: Chapter 6 2 (Classical) planning • Intelligent agents have goals •Plans are sequences of actions allowing to achieve those goals, from a given initial state
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