### 计算机代写|机器学习代写machine learning代考|COMP4702

statistics-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|Decision fn is degree-p polynomial

E.g., a cubic in $\mathbb{R}^2$ :
\begin{aligned} & \Phi(x)=\left[\begin{array}{lllllllll} x_1^3 & x_1^2 x_2 & x_1 x_2^2 & x_2^3 & x_1^2 & x_1 x_2 & x_2^2 & x_1 & x_2 \end{array}\right]^{\top} \ & \Phi(x): \mathbb{R}^d \rightarrow \mathbb{R}^{O(d r)} \end{aligned}
[Now we’re really blowing up the number of features! If you have, say, 100 features per sample point and you want to use degree-4 decision functions, then each lifted feature vector has a length of roughly 4 million, and your learning algorithm will take approximately forever to run.]
[However, later in the semester we will learn an extremely clever trick that allows us to work with these huge feature vectors very quickly, without ever computing them. It’s called “kernelization” or “the kernel trick.” So even though it appears now that working with degree- 4 polynomials is computationally infeasible, it can actually be done quickly.]

[Increasing the degree like this accomplishes two things.

• First, the data might become linearly separable when you lift them to a high enough degree, even if the original data are not linearly separable.
• Second, raising the degree can widen the margin, so you might get a more robust decision boundary that generalizes better to test data.

However, if you raise the degree too high, you will overfit the data and then generalization will get worse.]

[You should search for the ideal degree -not too small, not too big. It’s a balancing act between underfitting and overfitting. The degree is an example of a hyperparameter that can be optimized by validation.]
[If you’re using both polynomial features and a soft-margin SVM, now you have two hyperparameters: the degree and the regularization hyperparameter $C$. Generally, the optimal $C$ will be different for every polynomial degree, so when you change the degree, you have to run validation again to find the best $C$ for that degree.]
[So far I’ve talked only about polynomial features. But features can get much more complicated than polynomials, and they can be tailored to fit a specific problem. Let’s consider a type of feature you might use if you wanted to implement, say, a handwriting recognition algorithm.]

## 计算机代写|机器学习代写machine learning代考|Machine Learning Abstractions and Numerical Optimization

[When you write a large computer program, you break it down into subroutines and modules. Many of you know from experience that you need to have the discipline to impose strong abstraction barriers between different modules, or your program will become so complex you can no longer manage nor maintain it.]
[When you learn a new subject, it helps to have mental abstraction barriers, too, so you know when you can replace one approach with a different approach. I want to give you four levels of abstraction that can help you think about machine learning. It’s important to make mental distinctions between these four things, and the code you write should have modules that reflect these distinctions as well.]

[In this course, we focus primarily on the middle two levels. As a data scientist, you might be given an application, and your challenge is to turn it into an optimization problem that we know how to solve. We will talk a bit about optimization algorithms, but usually you’ll use an optimization code that’s faster and more robust than what you would write yourself.]
[The second level, the model, has a huge effect on the success of your learning algorithm. Sometimes you get a big improvement by tailoring the model or its features to fit the structure of your specific data. The model also has a big effect on whether you overfit or underfit. And if you want a model that you can interpret so you can do inference, the model has to have a simple structure. Lastly, you have to pick a model that leads to an optimization problem that can be solved. Some optimization problems are just too hard.]
[It’s important to understand that when you change something in one level of this diagram, you probably have to change all the levels underneath it. If you switch your model from a linear classifier to a neural net, your optimization problem changes, and your optimization algorithm changes too.]

[Not all machine learning methods fit this four-level decomposition. Nevertheless, for everything you learn in this class, think about where it fits in this hierarchy. If you don’t distinguish which math is part of the model and which math is part of the optimization algorithm, this course will be very confusing for you.]

# 机器学习代考

## 计算机代写|机器学习代写machine learning代考|Decision fn is degree-p polynomial

[现在我们真的在炸毁功能的数量! 如果你有，比如说，每个样本点有 100 个特征，并且你想使用 4 阶决 策函数，那么每个提升特征向量的长度大约为 400 万，你的学习算法将需要大约永远运行。]
[但是，在本学期的晩些时候，我们将学习一个非常聪明的技巧，使我们能够非常快速地处理这些巨大的 特征向量，而无需计算它们。它被称为“内核化“或“内核技巧”。因此，尽管现在看来使用 4 次多项式在计 算上是不可行的，但实际上可以很快完成。]
[像这样增加度数可以完成两件事。

• 首先，当您将数据提升到足够高的程度时，数据可能会变得线性可分，即使原始数据不是线性可分 的。
• 其次，提高度数可以扩大边际，因此您可能会得到更强大的决策边界，可以更好地泛化到测试数 据。
然而，如果你把度提高得太高，你会过度拟合数据，然后泛化会变得更糟。]
[你应该寻找理想的程度一一不要太小，也不要太大。这是欠拟合和过度拟合之间的平衡行为。度数是可 以通过验证优化的超参数示例。]
[如果您同时使用多项式特征和软间隔 SVM，那么现在您有两个超参数：度数和正则化超参数 C. 一般来 说，最优 $C$ 每个多项式的次数都会不同，所以当你改变次数时，你必须再次运行验证才能找到最好的 $C$ ] [到目前为止，我只讨论了多项式特征。但是特征可能比多项式复杂得多，并且可以对其进行定制以适应 特定问题。让我们考虑一种你可能会使用的特性，如果你想实现，比如说，手写识别算法。]

## 计算机代写|机器学习代写machine learning代考|Machine Learning Abstractions and Numerical Optimization

[当你写一个大型计算机程序时，你把它分解成子程序和模块。你们中的许多人从经验中知道，您需要遵守纪律，在不同模块之间强加强大的抽象障碍，否则您的程序将变得如此复杂，以至于您无法再管理或维护它。]
[当您学习一门新学科时，它有助于也有心理抽象障碍，所以你知道什么时候可以用另一种方法代替一种方法。我想给你四个抽象层次来帮助你思考机器学习。区分这四件事很重要，您编写的代码也应该有反映这些区别的模块。]

[在本课程中，我们主要关注中间两个级别。作为一名数据科学家，您可能会得到一个应用程序，而您的挑战是将其转化为我们知道如何解决的优化问题。我们将讨论一些优化算法，但通常您会使用比您自己编写的代码更快、更健壮的优化代码。]
[第二个层次，模型，对你学习算法的成功有巨大的影响。有时，您可以通过定制模型或其特征来适应特定数据的结构，从而获得很大的改进。该模型对您是否过拟合或欠拟合也有很大影响。如果您想要一个可以解释的模型以便进行推理，那么该模型必须具有简单的结构。最后，您必须选择一个可以解决优化问题的模型。有些优化问题实在是太难了。]
[重要的是要理解，当您更改此图的一个级别中的某些内容时，您可能必须更改它下面的所有级别。如果你将你的模型从线性分类器切换到神经网络，你的优化问题就会改变，你的优化算法也会改变。]

[并非所有的机器学习方法都适合这种四级分解。尽管如此，对于您在本课程中学到的所有内容，请考虑它在该层次结构中的位置。如果您不区分哪些数学是模型的一部分，哪些数学是优化算法的一部分，那么本课程会让您感到非常困惑。]

## 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。