ECS 20 — Lecture 17b = Discussion D8 — Fall 2013 —25 Nov 2013 Phil Rogaway Today: Using discussion section to finish up graph theory. Much of these notes the same as those prepared for last lecture and the lecture before. Graph Theory, continued Graph theory Graph theory 1. Basic definitions 2. Isomorphism 3. […]
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CS 70 Discrete Mathematics for CS Spring 2008 Note 8 Cake Cutting and Fair Division Algorithms The cake-cutting problem is as follows. We have a cake, to be shared among n of us, and we want to split it amongst themselves fairly. However, each person might value different portions of the cake differently. (I like
Today: o Regular expressions Distinguished Lecture after class : ECS 20 — Lecture 9 — Fall 2013 —24 Oct 2013 o Sets of strings (languages) “Some Hash-Based Data Structures and Algorithms Everyone Should Know” Prof. , Harvard Sets of STRINGS (elements of formal language theory) Define and give examples: Alphabet a finite nonempty set (of
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§5.1 Introduction §5.2 Motivation §5.3 Expectation-Maximization §5.4 Derivation of the EM §5.5 Newton-Raphson and Fish Missing Data and EM MAST90083 Computational Statistics and Data Mining School of Mathematics & Statistics The University of Melbourne Missing Data and EM 1/47 §5.1 Introduction §5.2 Motivation §5.3 Expectation-Maximization §5.4 Derivation of the EM §5.5 Newton-Raphson and Fish Outline
§5.1 Introduction §5.2 One Dimensional Kernel §5.3 Local Polynomial Regression §5.4 Generalized Additive Models Kernel and Local Regression MAST90083 Computational Statistics and Data Mining School of Mathematics & Statistics The University of Melbourne Kernel and Local Regression 1/42 §5.1 Introduction §5.2 One Dimensional Kernel §5.3 Local Polynomial Regression §5.4 Generalized Additive Models Outline §5.1 Introduction
Admin Overview Basic concepts Introduction MAST90083 Computational Statistics and Data Mining Dr Karim Seghouane School of Mathematics & Statistics The University of Melbourne Introduction 1/25 Admin Overview Basic concepts Outline §i. Admin §ii. Introduction & overview §iii. Basic concepts Introduction 2/25 Admin Overview Basic concepts Admin Lectures – Dr Karim Seghouane Mon. 14:15-16:15
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School of Mathematics and Statistics MAST90083: Computational Statistics and Data Science Assignment 2 Weight: 15% Some details about Question 1 and 2 For both questions, use library ”HRW” that contains the ”WarsawApts” dataset. The sym- bol n represents length of the variables for the given dataset (WarsawApts), and a bold 1 represents vector of ones.
MULT20015 Elements of Quantum Computing Lecture 12 Subject outline Lecture topics (by week) 1 – Introduction to quantum computing and maths basics 2 – Single qubit representations and logic operations 3 – Two qubit states and logic gates 4 – Multi-qubit states and quantum arithmetic 5 – Basic quantum algorithms 6 – Period finding, cryptography
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MULT20015 – Elements of Quantum Computing Lecture 18 Subject outline Lecture topics (by week) 1 – Introduction to quantum computing and maths basics 2 – Single qubit representations and logic operations 3 – Two qubit states and logic gates 4 – Multi-qubit states and quantum arithmetic 5 – Basic quantum algorithms 6 – Period finding,
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Game playing Chapter 6 Chapter 6 1 ♦ Games ♦ Perfect play – minimax decisions – α–β pruning ♦ Resource limits and approximate evaluation ♦ Games of chance ♦ Games of imperfect information Outline Chapter 6 2 Games vs. search problems “Unpredictable” opponent ⇒ solution is a strategy specifying a move for every possible opponent
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