ME001 Information Systems Analysis and Design Mini-project for optimal sample selection It is known that the amount of data has been increasing tremendously in the last few years due to the ease of accessing to the internet, cheap or inexpensive mass storage devices, the ease of transferring data through internet, communication lines and digital data […]
CS计算机代考程序代写 database data mining ME001 Read More »
PowerPoint Presentation Recap from Week 2 Population and Samples Measure data based on location (Mean, Median, Mode) Measure data based on dispersion (SD, Variance, Skewness, Coefficient of Variance, z score) Relationship between two variables (Covariance, Coefficient) Sampling method & Sampling distribution Confidence Internal & Hypotheses Testing Statistical Symbols for Populations and Samples Sample Population Mean
CS计算机代考程序代写 data mining Excel PowerPoint Presentation Read More »
COMP3308/3608, Lecture 12 ARTIFICIAL INTELLIGENCE Unsupervised Learning (Clustering) , COMP3308/3608 AI, week 12, 2022 1 Copyright By PowCoder代写 加微信 powcoder Announcement • Well done for completing Assignment 2! • Report part – we have started marking it in Canvas and aim to finish this by Sunday week 13 • Code part – automarked in Grok
CS代考 COMP3308/3608, Lecture 12 Read More »
COMP3308/3608 Artificial Intelligence Week 3 Tutorial exercises A* algorithm. Heuristics. Local search algorithms. Exercise 1 (Homework). A* search Copyright By PowCoder代写 加微信 powcoder Consider the tree below. Step costs are shown along the edges and heuristic values h are shown in brackets. The goal nodes are circled twice, i.e. they are C, I, E and
代写代考 COMP3308/3608 Artificial Intelligence Read More »
DATA MINING AND MACHINE LEARNING (EBUS537) Formative Assignment Set by Prof Dongping SONG Date of issue: 23rd Oct 2021. Date of submission: 19th November 2021 before 12 noon (online) Contribution: 0%. Essay length: 1000 words (maximum). Coursework: Using the given table as the training dataset, apply the Greedy strategy combined with the Gini impurity measure
CS计算机代考程序代写 data mining decision tree DATA MINING AND MACHINE LEARNING (EBUS537) Read More »
MAST90083 Computational Statistics & Data Mining KR and GAM Tutorial & Practical 6: Local & Kernel Regression (KR) and Generalized Additive Models (GAM) For this practical, generate a sinusoid by extending the curvy dataset from the last practical to 250 samples and increase the range of uniform distribution for noisy data from 1 to 5
CS计算机代考程序代写 data mining MAST90083 Computational Statistics & Data Mining KR and GAM Read More »
MAST90083 Computational Statistics & Data Mining Regression Splines Figure 1: Solution of Question 1 rm( l i s t=l s ( ) ) # c l e a r a l l the v a r i a b l e s in conso l e l i b r a r y ( s
MAST90083 Computational Statistics & Data MiningNonparametric Regression Tutorial & Practical 7: Nonparametric Regression Question 1 Consider the ordinary nonparametric regression model yi = f(xi) + �i; 1 ≤ i ≤ n where yi ∈ R, xi ∈ R, �i ∈ R ∼ N (0, σ2) and are i.i.d. For approximating f we propose to use
MAST90083 Computational Statistics & Data Mining Bootstrap Methods Tutorial & Practical 9: Solutions Question 1 1. Given X = {x1, …, xn}, with µ = E (xi) θ̂ = θ (F1) = [∫ x ( 1 n n∑ i=1 δ (x− xi) ) dx ]3 = [ 1 n n∑ i=1 xi ]3 = x̄3
MAST90083 Computational Statistics & Data Mining Regression Splines Tutorial & Practical 5: Regression Splines The implementation of splines has been described in detail in your course book, here we are going to call built-in functions from R. Our aim in this tutorial is to use the different types of splines to estimate a smooth data