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计算机代写|机器学习代写Machine Learning代考|KIT315 Optimization

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计算机代写|机器学习代写Machine Learning代考|Optimization

A main design principle for ML methods is the formulation of ML problems as optimization problems [4]. The weather prediction problem above can be formulated as the problem of optimizing (minimizing) the prediction error for the maximum daytime temperature. Many ML methods are obtained by straightforward applications of optimization methods to the optimization problem arising from a ML problem (or application).

The statistical and computational properties of such ML methods can be studied using tools from the theory of optimization. What sets the optimization problems in ML apart from “plain vanilla” optimization problems (see Fig. 1.6a) is that we rarely have perfect access to the objective function to be minimized. ML methods learn a hypothesis by minimizing a noisy or even incomplete version (see Fig. 1.6b) of the actual objective which is defined using an expectation over an unknown probability distribution. Section 4 discusses methods that are based on estimating the objective function by empirical averages that are computed over a set of data points (forming a training set).

计算机代写|机器学习代写Machine Learning代考|Theoretical Computer Science

Practical ML methods form a specific subclass of computing systems. Indeed, ML methods apply a sequence of computational operations to input data. The result of these computational operations are the predictions delivered to the user of the ML method. The interpretation of $\mathrm{ML}$ as computational systems allows to use tools from theoretical computer science to study the feasibility and intrinsic difficulty of ML problems. Even if a ML problem can be solved in theoretical sense, every practical ML method must fit the available computational infrastructure [5, 6].

The available computational resources, such as processor time, memory and communication bandwidth, can vary significantly between different infrastructures. One example for such a computational infrastructure is a single desktop computer. Another example for a computational infrastructure is a cloud computing service which distributes data and computation over large networks of physical computers [7].

The focus of this book is on ML methods that can be understood as numerical optimization algorithms (see Chaps. 4 and 5). Most of these ML methods amount to (a large number of) matrix operations such as matrix multiplication or matrix inversion [8]. Numerical linear algebra provides a vast algorithmic toolbox for the design of such ML methods [3, 9]. The recent success of ML methods in several application domains might be attributed to their efficient use of matrices to represent data and models. Using this representation allows us to implement the resulting ML methods using highly efficient hard- and software implementations for numerical linear algebra [10].

计算机代写|机器学习代写Machine Learning代考|Optimization

$M L$ 方法的主要设计原则是将 ML 问题表述为优化问题 [4]。上面的天气预报问题可以表述为优化 (最小化) 最大白天温度的预则 误差的问题。许多 ML 方法是通过将优化方法直接应用于由 ML 问题（或应用程序）引起的优化问题而获得的。

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。