Instructions CSC373 Fall’20 Assignment 2: Crazy Circus Due Date: 1st November 2020, 11:59pm ET 1. Typed assignments are preferred (e.g., PDFs created using LaTeX or Word), especially if your handwriting is possibly illegible or if you do not have access to a good quality scanner. Either way, you need to submit a single PDF named […]
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CSC373 Week 5: Network Flow (contd) Nisarg Shah 373F20 – Nisarg Shah 1 Recap • Some more DP ➢ Traveling salesman problem (TSP) • Start of network flow ➢ Problem statement ➢ Ford-Fulkerson algorithm ➢ Running time ➢ Correctness using max-flow, min-cut 373F20 – Nisarg Shah 2 This Lecture • Network flow in polynomial time
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Vision & Language Applications Kate Saenko Machine Learning so far… General AI: machines that see, talk, act Action Vision AI Language ▪ ▪ ▪ ▪ ▪ Social media analysis Security and smart cameras AI assistants Helper robots for the elderly etc… More Natural Human-Machine Interaction Find a teapot on the table What is in this
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Ethics in Machine Learning Kate Saenko CS 542 Machine Learning A robot kills human yes, Uber car accident AI takes over our lives yes? Youtube algorithm AI is watching us yes, virtual police lineup Autonomous weapons not yet? Humans losing jobs Yes, e.g. librarians AI Fears: Which of these has already happened? AI Fears •
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Count Sketch: Finding Heavy Hitters COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Auckland, Aug 24, 2020 1 Basic definitions Let U be a universe of size n, i.e. U = {1, 2, 3, …, n}. Cash register model stream: Sequence of m elements a1,…, am where ai
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Universal Hashing COMPCSI 753: Algorithms for Massive Data Ninh Pham University of Auckland Parts of this material are modifications of the lecture slides of Kevin Wayne (https://www.cs.princeton.edu/~wayne/kleinberg-tardos/) Designed for the textbook Algorithm Design by Jon Kleinberg and Eva Tardos. Auckland, Aug 3, 2020 1 Dictionary data type Dictionary problem: Given a universe U
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Misra-Gries Summary: Finding Heavy Hitters in Data Stream COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Auckland, Aug 18, 2020 1 Basic definitions Let U be a universe of size n, i.e. U = {1, 2, 3, … , n} Cash register model stream: Sequence of m elements
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CountMin Sketch: Finding Heavy Hitters COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Auckland, Aug 24, 2020 1 Basic definitions Let U be a universe of size n, i.e. U = {1, 2, 3, … , n}. Cash register model stream: Sequence of m elements a1, … ,
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Bloom Filter COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Parts of this material are modifications of the lecture slides from http://mmds.org Designed for the textbook Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman. Auckland, Aug 18, 2020 1 Basic definitions Let U be a universe
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Sampling Techniques COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Parts of this material are modifications of the lecture slides from http://mmds.org Designed for the textbook Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman. Auckland, Aug 17, 2020 1 Outline Sampling from a data stream
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