SIT384 Cyber security analytics Credit Task 9.1C: Grid Search with Cross-Validation Task description: C and Gamma are the parameters for a nonlinear support vector machine (SVM). The goal of SVM is to find a hyperplane that would leave the widest possible “cushion” between input points from two classes. There is a tradeoff between “narrow cushion, […]
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SIT384 Cyber security analytics Pass Task 7.1P: K-Means and Hierarchical Clustering Task description: In machine learning, clustering is used for analyzing and grouping data which does not include pre- labeled class or even a class attribute at all. K-Means clustering and hierarchical clustering are all unsupervised learning algorithms. K- means is a collection of objects
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Assignment 3 Due Date & Time: Maximum Weight: Submission: Assignment Type: KXO151 Programming & Problem Solving AIEN-SOU – 2019 10pm (Shanghai) Friday, Week 14, 31 May, 2019 30% (of the total assessment for KXO151) Via MyLO NOTE: All assignments will be checked for plagiarism by a specialist Java program that checks your assignment against other
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ABALONE RINGS DETECTION USING AI Student name-Student ID Abstract— Briefly explain what you have done. • Introduction Introduce the problem you are solving, what is the impact? how you solved that using AI techniques? • Data Collection Explain what you did for Task 1 • DatA PROCESSING Explain what you did for Task 2 •
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KIT108 ARTIFICIAL INTELLIGENCE Assignment Due Friday, 31st May 2019 TASMANIA ABALONE RING PREDICTION Description In this assignment we will apply machine learning techniques learned in the lectures and tutorials to analyse the population biology of abalone in Tasmania. In particular, we will predict the age of abalone from physical measurements. By observation, the age of
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Computer Science 220S1C (2019) Assignment 3 (hashing and basic graph algorithms) Due date May 21, 2018, 10pm 100 Marks in total This assignment requires you to submit programs in Python that you have written yourself to the automarker, http://www.cs.auckland.ac.nz/automated-marker. Your imple- mentation must be from first principles and cannot use an existing library methods that
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ANUC 1100 – Introduction to Programming and Algorithms Semester 1, 2019 Assignment 2 Question 1 Prime Number List (50%) A prime number is an integer number greater than 1 whose only factors are 1 and itself. Your task is to find out all prime numbers between 1 and 100,000. And save all those prime numbers
1.Exercise 1 Frequent Itemsets 在本练习中，您必须阅读第6.4节至6.4.3.1。 • 实现6.4.12中给出的简单随机算法。 • 在6.4.33中实现Savasere，Omiecinski和Navathe（SON al-gorithm）的算法。 • 比较数据集T10I4D100K，T40I10D100K，chess, connect, mushroom, pumsb, pumsb star的两种算法，并提供http://fimi.ua.ac.be/data/并报告结果。 • 在简单的随机算法中测试不同的样本大小，例如1,2,5,10％并比较您的结果（包括SON算法产生的结果）。您的方法应该在运行时方面尽可能高效 记忆要求。报告您在实施过程中可能遇到的挑战以及运行实验。 2. Exercise 2 Clustering 1.对一维点集1,4,4,16,25,36,49,64,81执行层次聚类。 假设聚类由它们的质心（平均值）表示，并且在步骤中合并具有最接近质心的聚类。（Exercise7.2.1） 2.实现K-means算法并在提供的Iris数据集上进行实验。 a）要求您通过绘制输入数据的前2个维数以及收敛质心来绘制K均值结果。 b）提供一些关于如何在K-means中选择K值的讨论。对于Iris数据，仅使用前4个维度进行此练习。 换句话说，丢弃标签信息。 Exercise 3 Advertising 考虑例8.7。 假设有三个广告商A，B和C. 有三个查询x，y和z。 每位广告客户的预算为2。 Advertiser A only bids on x, B bids on x and y, and C bids on x, y,
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COMPSCI 130 – Semester 1 – 2019 Deadline: 11:59pm, Friday 7th June Worth: 10% of your final grade Project Two Creatures COMPSCI 130, Semester 1, 2019 – 1 – Project Two Getting started To get started, download the resource file (“ProjectTwoCreatures.zip”) from Canvas. If you run the provided program “Creatures.py” you will see the following
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Faculty of Engineering and Information Technology School of Software 42028: Deep Learning and Convolutional Neural Networks Autumn 2019 ASSIGNMENT-2 SPECIFICATION Due date Friday 11:59pm, 31 May 2019 Demonstrations Marks Submission Optional, If required. 40% of the total marks for this subject 1. AreportinPDForMSWorddocument(10-pages) 2. GoogleColab/iPythonnotebooks Submit to Note: This assignment is individual work. UTS Online