08.key http://people.eng.unimelb.edu.au/tobym @tobycmurray toby.murray@unimelb.edu.au DMD 8.17 (Level 8, Doug McDonell Bldg) Toby Murray COMP90038 Algorithms and Complexity Lecture 8: Graph Traversal (with thanks to Harald Søndergaard) Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Breadth-First and Depth-First Traversal • Used to systematically explore all nodes of a graph 2 a […]
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NLP Assignment (2018)-checkpoint In [ ]: # coding: utf-8 from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf import numpy as np import collections import math import os import random from nltk import word_tokenize from collections import namedtuple import sys, getopt from random import shuffle # Constants label_to_id = {‘World’:0,
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COMP8551 Heterogenous computing COMP 8551 Advanced Games Programming Techniques Heterogenous computing Borna Noureddin, Ph.D. British Columbia Institute of Technology Overview •Motivation •Parallel software •Data-parallel • Task-parallel •Heterogenous computing 2 © B or na N ou re dd in C O M P 85 51 Motivation • Programming computers composed of a combination of CPUs, GPUs,
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1 CS402: Seminar 2 An Introduction to OpenMP OpenMP (Multiprocessing) is an API and runtime which enables the pro- gramming of multiple processing cores with shared memory. The API is a collection of functions and pragmas, the former allows the querying of informa- tion, such as the number of active threads; and the latter allows
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Computer Networking A Top-Down Approach 6th Edition James F. Kurose University of Massachusetts, Amherst Keith W. Ross Polytechnic Institute of NYU COMPUTER NETWORKING A Top-Down Approach SIXTH EDITION Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montréal Toronto Delhi Mexico City São Paulo Sydney
Slide 1 BUSINESS SCHOOL Discipline of Business Analytics QBUS6850 Team Topics covered k-Nearest-Neighbours Algorithm Unsupervised learning introduction K-means References Friedman et al., (2001), Chapters 13.1 – 13.3, 14.3 James et al., (2014), Chapter 10.3 Bishop, (2006), Chapter 2.5.2, 9.1 2 Learning Objectives Understand the intuition
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18s1: COMP9417 Machine Learning and Data Mining Assignment 2 – Introduction May 7, 2018 Aims Learning objectives of this assignment: • a self-selected task to extend aspects of the course material • involves practical aspects of the machine learning problem, i.e. – implementing or modifying algorithms and/or – experimental evaluation of algorithms on data set(s)
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COMP9313 2018s1 Project 2 Problem statement – single target shortest path: Given a graph and a node “t”, find the shortest distances of all nodes to “t” together with the paths. For example, the shortest distance from node 1 to t is 7 with path 1->3->4->t. Please note that this is different from the single-
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Better Search Performance Performance What is the worst case time complexity of Binary Search? Binary Search(a[1, . . . ,N], k) Cost Executions l = 1, r = N + 1 c1 1 while l < r c2 ?? m = l + (r-l) / 2 c3 ?? if (k == a[m]) c4 ?? return
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6G6Z1109: So+ware Agents and Op8misa8on Term 2, Lecture 7: Local Search Local search • For many problems, we can improve the quality of gene8c search by somehow embedding knowledge of the problem into the algorithm • Benefits: – Reduces the probability of “illegal” solu8ons – Simplifies the representa8on scheme – Speeds up search/reduces size of solu8on space An
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