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assignmentutor-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|机器学习代写machine learning代考|Curse of Dimensionality

In machine learning, the curse of dimensionality refers to the dilemma of learning in high-dimensional spaces. As shown in the previous k-NN example, as the dimensionality of learning problems grows, the volume of the underlying space increases exponentially. This typically requires an exponentially increasing amount of training data and computing resources to ensure the effectiveness of any learning methods. Moreover, our intuition of the three-dimensional physical world often fails in high dimensions [54]. The similarity-based reasoning breaks down in high dimensions as the distance measures become unreliable and counterintuitive. For example, if many samples are uniformly placed inside a unit hypercube in a high-dimensional space, it is proven that most of these samples are closer to a face of the hypercube than to their nearest neighbors.

However, the worst-case scenarios predicted by the curse of dimensionality normally occur when the data are uniformly distributed in highdimensional spaces. Most real-world learning problems involve highdimensional data, but the good news is that real-world data never spread evenly throughout the high-dimensional spaces. This observation is often referred to as the blessing of nonuniformity [54]. The blessing of nonuniformity essentially allows us to be able to effectively learn these highdimensional problems using a reasonable amount of training data and computing resources. A nonuniform data distribution suggests that all dimensions of the data are not independent but highly correlated in such a way that many dimensions are redundant. In other words, many dimensions can be discarded without losing much information about the data distribution. This idea motivates a group of machine learning methods called dimensionality reduction. Alternatively, a nonuniform distribution in a high-dimensional space also suggests that the real data are only concentrated in a linear subspace or a lower-dimensional nonlinear subspace, which is often called a manifold. In machine learning, the so-called manifold learning aims to identify such lower-dimensional topological spaces where high-dimensional data are congregated.

## 计算机代写|机器学习代写machine learning代考|Vectors and Matrices

A scalar is a single number, often denoted by a lowercase letter, such as $x$ or $n$. We also use $x \in \mathbb{R}$ to indicate that $x$ is a real-valued scalar and $n \in \mathbb{N}$ for that $n$ is a natural number. A vector is a list of numbers arranged in order, denoted by a lowercase letter in bold, such as $\mathbf{x}$ or $\mathbf{y}$. All numbers in a vector can be aligned in a row or column, called a row vector or column vector, accordingly. We use $\mathbf{x} \in \mathbb{R}^n$ to indicate that $\mathbf{x}$ is an $n$-dimensional vector containing $n$ real numbers. This book adopts the convention of writing a vector in a column, such as the following:
$$\mathbf{x}=\left[\begin{array}{c} x_1 \ x_2 \ \vdots \ x_n \end{array}\right] \quad \mathbf{y}=\left[\begin{array}{c} y_1 \ y_2 \ \vdots \ y_m \end{array}\right] .$$
A matrix is a group of numbers arranged in a two-dimensional array, often denoted by an uppercase letter in bold, such as A or B. For example, a matrix containing $m$ rows and $n$ columns is called an $\mathrm{m} \times \mathrm{n}$ matrix,

represented as
$$\mathbf{A}=\left[\begin{array}{cccc} a_{11} & a_{12} & \cdots & a_{1 n} \ a_{21} & a_{22} & \cdots & a_{2 n} \ \vdots & \vdots & \ddots & \vdots \ a_{m 1} & a_{m 2} & \cdots & a_{m n} \end{array}\right] .$$
We use $\mathbf{A} \in \mathbb{R}^{m \times n}$ to indicate that $\mathbf{A}$ is an $m \times n$ matrix containing all real numbers.

# 机器学习代考

## 计算机代写|机器学习代写machine learning代考|Vectors and Matrices

$$\mathbf{x}=\left[x_1 x_2 \vdots x_n\right] \quad \mathbf{y}=\left[y_1 y_2 \vdots y_m\right]$$

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师