STAT318/462 — Data Mining Dr G ́abor Erd ́elyi University of Canterbury, Christchurch, Course developed by Dr B. Robertson. Some of the figures in this presentation are taken from “An Introduction to Statistical Learning, with applications in R” (Springer, 2013) with permission from the authors: G. James, D. Witten, T. Hastie and R. Tibshirani. G. […]
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COMP90038 Algorithms and Complexity Priority Queues, Heaps and Ohrimenko (Slides from Harald Søndergaard) Lecture 13 Semester 2, 2021 …………… . …. …………….. … Algorithms and Complexity (Sem 2, 2021) Priority Queues, Heaps and Heapsort © University of Melbourne 1 / 1 New lecturer Dr Olya Ohrimenko (part 2) PhD from Brown University 6 years Microsoft
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COMP90038 Algorithms and Complexity Lecture 7: Graphs and Graph Concepts (with thanks to Harald Søndergaard) DMD 8.17 (Level 8, Doug McDonell Bldg) http://people.eng.unimelb.edu.au/tobym @tobycmurray 2 Graphs and Trees One instance of the exhaustive search paradigm is graph traversal. • After this lecture we shall look at two ways of systematically visiting • every node of
Computer Vision (7CCSMCVI / 6CCS3COV) Recap • Image formation ● Low-level vision ● Mid-level vision ● grouping and segmentation of image elements ● Biological ● bottom-up influences (Gestalt cues) ● top-down influences (knowledge, expectation, etc.) ● Artificial ● thresholding, region-based, clustering, fitting ● Multi-View Vision ● correspondence problem ● stereo ● video ● High-level vision
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1. Introduction and JUnit Module 1 Part 1 of 2 Introduction and Agents 1 1 Class Learning Objectives Understand objectives of AI Apply agents 2 The word “AI” has become ubiquitous. But what, exactly, does it mean? This part answers that question. A good portion of AI approaches problems from the perspective of agents—autonomous objects
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High-Level Vision (Artificial) 1. Below is shown a template T and an image I . Calculate the result of performing template matching on the image, and hence, suggest the location of the object depicted in the template assuming that there is exactly one such object in the image. Use the following similarity measures (a) normalised
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CSCI 4061 Introduction to Operating Systems Instructor: General Information Lectures: Sec 1: Tu/Th 8.15 am-9.30 am Sec 10: Tu/Th 1pm-2.15pm Course Modality: Lecture, labs in-person (subject to change) Live streamed and recorded lectures, labs All office hours via zoom Course material and information: Via Canvas page
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PowerPoint Presentation Faculty of Information Technology, Monash University FIT2004: Algorithms and Data Structures Week 9: Bellman-Ford and Floyd-Warshall Algorithms These slides are prepared by M. A. Cheema and are based on the material developed by and . Recommended reading FIT2004, Lec-9: Bellman-Ford and Floyd-Warshall Algorithms Unit notes: Chapter 13 Cormen et al. Introduction to Algorithms.
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End-of-year Examinations, 2020 STAT318/STAT462-20S2 (C) Family Name First Name Student Number Venue Seat Number _____________________ _____________________ |__|__|__|__|__|__|__|__| ____________________ ________ No electronic/communication devices are permitted. No exam materials may be removed from the exam room. Mathematics and Statistics EXAMINATION End-of-year Examinations, 2020 STAT318-20S2 (C) / STAT462-20S2 (C) Data Mining Examination Duration: 120 minutes Exam Conditions: Closed
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COMP90038 Algorithms and Complexity Lecture 3: Growth Rate and Algorithm Efficiency (with thanks to Harald Søndergaard) DMD 8.17 (Level 8, Doug McDonell Bldg) http://people.eng.unimelb.edu.au/tobym @tobycmurray Algorithm Efficiency Two algorithms for computing gcd: Why is one more efficient than the other? What does “efficient” even mean? How can we talk about these things precisely? 2 Copyright