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

Many advances have been made in recent years based on machine learning, in particular with deep learning methods for image processing, natural language processing, and more general data analytics. Many companies are now enthusiastic about data analytics, using data in a wider sense to gain insights into customer profiles or other data mining tasks. Machine learning is an important part of a data analytics engine. Data analytics often require additional care such as data security to ensure privacy, the ability to acquire and maintain large data collections, and also to make results available in a form useful for humans. We will not delve into many of these aspects but concentrate instead on the data modeling aspects.

One of the most visible impacts of deep learning has been made in computer vision through convolutional neural networks. The basic applications in this area are mostly based on recognition networks and methods for semantic segmentation. However, such methods have now also advanced object localization, object tracking, and scene understanding, to name but a few. Some examples from my own projects are shown in Fig. 1.7. The left-hand image shows semantic segmentation to identify and localize crop and weed for a robotic farming application. The right-hand image shows an application of fish tracking for aquaculture applications.Another area that has seen a huge improvement is the area of natural language processing (NLP). It has long been an important tasks to build programs that understand natural languages for applications such as translation, sentiment analysis, or to enable

some form of formal analysis of technical reports. Various methods for sequence modeling have contributed greatly to this area, in particular recurrent neural networks, discussed later in this book.

A developing area in machine learning are generative models. Generative models are models that can make examples of instances of a class. For example, a generative models can learn about cars from examples and then generate images of new cars by itself. Such networks could then be used in some creative way. Examples of systems that can learn generative models are variational autoencoders (VAEs) and generative adversarial networks (GANs). These methods demonstrate an important advance: the ability to capture the probabilistic structure of objects which in turn can be exploited in various ways.

Machine learning methods have shown that it can produce solutions to problems that have previously been intractable. For example, computer programs to play the Chinese board game “Go” have been mostly available only at an advance novice level until a few years ago. However, in 2016 , a machine learning program called “Alpha-Go” that combined cleverly supervised and reinforcement learning was able to beat a player, Mr. Lee Sedol, who is considered one of the best players of the last decade and had previously won sixteen world titles. Go was considered to be a real challenge for AI systems as it was considered to rely a lot on “gut feelings” rather than quantifiable strategies. It was therefore a huge success when computers, which had only reached levels of an advanced beginner a few years prior, could win against such an accomplished player.

## cs代写|机器学习代写machine learning代考|No free lunch, but worth the bite

Neural networks and other models, such as support vector machines and decision trees, are fairly general models in contrast to Bayesian models that are usually much better at specifying a causal structure of interpretable entities. More specific models should outperform more general model as long as they faithfully represent the underlying structure of the world model. This fact is captured by David Wolpert’s “No free lunch” theorem, which states that there is not a single algorithms that covers all applications better than some other algorithms. The best model is, of course, the real world model, as discussed earlier, which we generally do not know. Applying machine learning algorithms is therefore somewhat of an art and requires experience and knowledge of the constraints of the algorithms. Discussions of what is an appropriate model are

sometimes cumbersome and can distract us from making good use of them. We take a more practical approach, letting a user define what an appropriate contribution is for a machine learning model. For example, the best accuracy of a prediction might not always be the goal, and other considerations such as the speed of processing, the number of required training data, or the ability to interpret data can be important factors. We will therefore include brief discussions of some classic machine learning algorithms even if they do not represent the latest research in this area.

An interesting remark that often cops up in discussions of some machine learning algorithms and, in particular, neural networks is that these methods are commonly described, and somewhat criticized, as being black box methods. By “back box” we usually mean that the internal structure is not known. However, the machine learning models usually live in a computer where we can inspect all the components; these methods are hence known as white box methods. A better way to describe the difficulties with the ability human have in interpreting machine learning models is due to the fact that trained deep learning models are commonly complex models that implement complex decision rules. While some application might have as a goal the learning of human interpretable decision rules, other might rather be interested in achieving better prediction performance, which often requires more fine-grained rules.
We will see in Chapter 3 that writing a program to apply machine learning algorithms to data is often not very difficult. New algorithms will often find their way to graphical data mining tools, which makes them available to an even larger application community. However, applying such algorithms correctly in different application domains can be challenging and it is well known that some experience is required. We therefore concentrate in the following on explaining what is behind these algorithms and how different theoretical concepts are explored by them. Some understanding of the algorithms is absolutely necessary to avoid pitfalls in their application.

The basic first step for the application of ML methods is how to represent the data. We mentioned already some different data structures of inputs such as vectors or tensors. However, there are usually many different possible ways to represent a problem numerically. In the past it has been crucial to work out an appropriate highlevel data representation such as summary statistics to keep the dimensionality of the model low. However, the recent progress in deep learning made it possible to treat this representation itself as part of the learning problem. Representational learning has thus become an important part of machine learning.

## cs代写|机器学习代写machine learning代考|Programming environment

We will be using a programming environment called Jupyter. Specifically, we will be using the Jupyter notebook that allows us to write code with a simple editor and display comments and outputs in the same file. Jupyter is accessed through the browser and contains form fields in which code and comments can be added. These fields can then be executed and the feedback from print commands or figure plots are displayed after each block within the same document. This makes it very useful in documenting brief code and small exercises. An example program is shown in Fig. 2.1. All example programs in this book are available as Jupyter files on the web.

The Jupyter notebook has an interface to launch the Python interpreter and to run individual sections or all the code. The header with comments is produced by executing a text cell. This is useful to produce some documentations. Also, the notebook can be distributed with the output that can facilitate communications about code. The numbers on the left shows a consecutive number of calls to the interpreter. In the shown example, the first program cell was run first to load the libraries, and then the second cell was run twice; this is why a [3] is displayed in front of this cell. When the program is running, an $[*]$ is displayed. The second cell produces the output 4 , which is displayed after the cell.

A more advanced environment for bigger programs with more traditional programming support is Spyder. This tool includes an editor, a command window, and further programming support such as displays of variables and debugging support. This pro-gram mimics more traditional programming environment such as the ones found in Matlab and R. An example view of Spyder is shown in Fig. 2.2. On the left is the editor window that contains a syntax-sensitive display to write the programs, and on the right is the console to launch line commands such as executing and interpreting the code. As Python is an interpreted language, it is possible to work with the programs in an interactive way, such as running a simulation and than plotting results in various ways. The Spyder development environment is recommended for bigger projects.

## cs代写|机器学习代写machine learning代考|Programming environment

Jupyter notebook 有一个接口来启动 Python 解释器并运行各个部分或所有代码。带有注释的标题是通过执行文本单元格生成的。这对于生成一些文档很有用。此外，notebook 可以与可以促进代码交流的输出一起分发。左侧的数字显示了对口译员的连续呼叫次数。在所示示例中，第一个程序单元首先运行以加载库，然后第二个单元运行两次；这就是为什么在此单元格前面显示 [3] 的原因。当程序运行时，一个[∗]被展示。第二个单元格产生输出 4，显示在单元格之后。

Spyder 是为具有更传统编程支持的大型程序提供的更高级环境。该工具包括一个编辑器、一个命令窗口和进一步的编程支持，例如变量显示和调试支持。该程序模仿了更传统的编程环境，例如 Matlab 和 R 中的编程环境。Spyder 的示例视图如图 2.2 所示。左侧是编辑器窗口，其中包含用于编写程序的语法敏感显示，右侧是控制台，用于启动行命令，例如执行和解释代码。由于 Python 是一种解释型语言，因此可以以交互方式处理程序，例如运行模拟并以各种方式绘制结果。大型项目推荐使用 Spyder 开发环境。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。