» Assignments » Assignment 3: Ataxx In this assignment, you will develop an AI that plays Ataxx, a strategy board game from 1990. We have implemented the rules of the game for you, but you will have to decide how best to play the game. This assignment is worth 15% of your final grade. Deadline: […]
1. Introduction and JUnit Search 1 A key perspective for AI an application is to interpret it as searching for a solution. For example, answering the question “How should I furnish my living room?” A systematic search algorithm that’s certain to yield all solutions is called brute force. Such algorithms do not, in general, account
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1. Introduction and JUnit Natural Language 1 This module explores the relationship of AI to natural (i.e., ordinary) language. NL has always been part of AI but two things have have recently made an NL a key technology—if not part of our lives. The first is the recognition that machine learning is extraordinarily helpful for
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PowerPoint 演示文稿 概率论序言 在自然界和人类社会中有两类现象: (1)确定性现象. 例如: 在标准大气压下，纯水加热到100℃必然沸腾; 向空中抛掷的物体必然会下落; 太阳每天必然从东边升起，西边落下； 等等…… 可能出现的结果: “1”, “2”, “3”, “4”, “5”或“6”。 实例1 “抛掷一粒骰子,观察出现的点数”。 （2）随机现象: 实例2 “医院中出生的宝宝的性别”。 女,男。 可能出现的结果： 实例3 “观察明天的天气”。 可能出现的结果： 晴，多云，雨。 第一节 随机试验 确定性现象： 随机现象：在个别或者少量试验中无规律，但大量重复试验，其结果具有统计规律性（投掷硬币实验） 随机试验：可重复性，可观察性，随机性 第二节 样本空间、随机事件 样本点e . S 现代集合论为表述随机试验提供了一个方便的工具 . 一、样本空间 例如,试验是将一枚硬币抛掷两次,观察正面H、反面T出现的情况: S={(H,H), (H,T), (T,H), (T,T)} 第1次 第2次 H H T H H T T T
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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
Probability Density Functions Australian National University (James Taylor) 1 / 7 6 I Density and Likelihood Functions First, suppose X is an (absolutely continuous) random variable Everything we will need to know about X is summarised by it’s probability density function (pdf) f (x) That is, given f (x) we can compute EX , Var(X
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Collinearity Australian National University (James Taylor) 1 / 5 4.0 KEKE Perfect Multicollinearity Perfect multicollinearity occurs in OLS problems when there is an exact linear relation among the regressors/explanatory data Mathematically, this is a huge problem, as the data matrix X is not ‘full rank’ Which means (X0X) is not invertible, Which means b̂ =
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Linear Algebra Review Australian National University (James Taylor) 1 / 6 b0 Linear Algebra Review Matrix multiplication Linear Transformations Transposition Determinants and Invertability (James Taylor) 2 / 6 Matrix Multiplication (James Taylor) 3 / 6 L it.I i I Linear Transformations (James Taylor) 4 / 6 I Dinention Def e f 212 IRis a lineartransformation
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Covariance Stationarity General Theory Australian National University (James Taylor) 1 / 5 8 I Covariance Stationarity Focusing now on cyclical models without drift Need to assume the underlying probabilistic structure doesn’t change If it changes, forecasting would not be possible Use covariance stationarity (weak second order stationarity) (James Taylor) 2 / 5 Covariance Stationarity –
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