### 统计代写|机器学习代写machine learning代考|Reinforcement Learning

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
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

## 统计代写|机器学习代写machine learning代考|Reinforcement Learning

The problem of reinforcement learning is to learn what to do-how to map situations to actions-so as to maximize a given reward. In contrast to the supervised learning task, the learning algorithm is not told which actions to take in a given situation. Instead, the learner is assumed to gain information about the actions taken by some reward not necessarily arriving immediately after the action is taken. One example of such a problem is learning to play chess. Each board configuration, i.e., the position of all figures on the $8 \times 8$ board, is a given state; the actions are the possible moves in a given position. The reward for a given action (chess move) is winning the game, losing it or achieving a draw. Note that this reward is delayed which is very typical for reinforcement learning. Since a given state has no “optimal” action, one of the biggest challenges of a reinforcement learning algorithm is to find a trade-off between exploration and exploitation. In order to maximize reward a learning algorithm must choose actions which have been tried out in the past and found to be effective in producing reward-it must exploit its current knowledge. On the other hand, to discover those actions the learning algorithm has to choose actions not tried in the past and thus explore the state space. There is no general solution to this dilemma, but that neither of the two options can lead exclusively to an optimal strategy is clear. As this learning problem is only of partial relevance to this book, the interested reader should refer Sutton and Barto (1998) for an excellent introduction to this problem.

## 统计代写|机器学习代写machine learning代考|Learning Kernel Classifiers

Here is a typical classification learning problem. Suppose we want to design a system that is able to recognize handwritten zip codes on mail envelopes. Initially, we use a scanning device to obtain images of the single digits in digital form. In the design of the underlying software system we have to decide whether we “hardwire” the recognition function into our program or allow the program to learn its recognition function. Besides being the more flexible approach, the idea of learning the recognition function offers the additional advantage that any change involving the scanning can be incorporated automatically; in the “hardwired” approach we would have to reprogram the recognition function whenever we change the scanning device. This flexibility requires that we provide the learning

algorithm with some example classifications of typical digits. In this particular case it is relatively easy to acquire at least $100-1000$ images and label them manually (see Figure $1.5$ (left)).

Our next decision involves the representation of the images in the computer. Since the scanning device supplies us with an image matrix of intensity values at fixed positions, it seems natural to use this representation directly, i.e., concatenate the rows of the image matrix to obtain a long data vector for each image. As a consequence, the data can be represented by a matrix $\mathbf{X}$ with as many rows as number of training samples and as many columns are there are pixels per image (see Figure $1.5$ (right)). Each row $\mathbf{x}_{i}$ of the data matrix $\mathbf{X}$ represents one image of a digit by the intensity values at the fixed pixel positions.

Now consider a very simple learning algorithm where we just store the training examples. In order to classify a new test image, we assign it to the class of the training image closest to it. This surprisingly easy learning algorithm is also known as the nearest-neighbor classifier and has almost optimal performance in the limit of a large number of training images. In our example we see that nearest neighbor classification seems to perform very well (see Figure 1.6). However, this simple and intuitive algorithm suffers two major problems:

1. It requires a distance measure which must be small between images depicting the same digit and large between images showing different digits. In the example shown in Figure 1.6 we use the Euclidean distance
$$|\mathbf{x}-\overline{\mathbf{x}}| \stackrel{\text { def }}{=} \sqrt{\sum_{j=1}^{N}\left(x_{j}-\tilde{x}_{j}\right)^{2}}$$where $N=784$ is the number of different pixels. From Figure $1.6$ we already see that not all of the closest images seem to be related to the correct class, which indicates that we should look for a better representation.

## 统计代写|机器学习代写machine learning代考|The Purposes of Learning Theory

The first part of this book may lead the reader to wonder-after learning so many different learning algorithms-which one to use for a particular problem. This legitimate question is one that the results from learning theory try to answer. Learning theory is concerned with the study of learning algorithms’ performance. By casting the learning problem into the powerful framework of probability theory, we aim to answer the following questions:

1. How many training examples do we need to ensure a certain performance?
2. Given a fixed training sample, e.g., the forty-nine images in Figure 1.5, what performance of the function learned can be guaranteed?
1. Given two different learning algorithms, which one should we choose for a given training sample so as to maximize the performance of the resulting learning algorithm?

I should point out that all these questions must be followed by the additional phrase “with high probability over the random draw of the training sample”. This requirement is unavoidable and reflects the fact that we model the training sample as a random sample. Thus, in any of the statements about the performance of learning algorithms we have the inherent duality between precision and confidence: The more precise the statement on the algorithm’s performance is, e.g., the prediction error is not larger than $5 \%$, the less confident it is. In the extreme case, we can say that the prediction error is exactly $5 \%$, but we have absolutely no (mathematical) confidence in this statement. The performance measure is most easily defined when considering supervised learning tasks. Since we are given a target value for each object, we need only to measure by how much the learned function deviates from the target value at all objects-in particular for the unseen objects. This quantity is modeled by the expected loss of a function over the random draw of object-target pairs. As a consequence our ultimate interest is in (probabilistic) upper bounds on the expected loss of the function learned from the random training sample, i.e., $\mathbf{P}$ (training samples s.t. the expected loss of the function learned $\leq \varepsilon(\delta)) \geq 1-\delta$.

## 统计代写|机器学习代写machine learning代考|Learning Kernel Classifiers

1. 它需要一个距离度量，在描绘相同数字的图像之间必须很小，而在显示不同数字的图像之间必须很大。在图 1.6 所示的示例中，我们使用欧几里得距离
|X−X¯|= 定义 ∑j=1ñ(Xj−X~j)2在哪里ñ=784是不同像素的数量。从图1.6我们已经看到，并非所有最接近的图像似乎都与正确的类相关，这表明我们应该寻找更好的表示。

## 统计代写|机器学习代写machine learning代考|The Purposes of Learning Theory

1. 我们需要多少训练样例才能保证一定的性能？
2. 给定一个固定的训练样本，例如图 1.5 中的 49 张图像，可以保证所学函数的性能如何？
3. 给定两种不同的学习算法，我们应该为给定的训练样本选择哪一种，以最大限度地提高学习算法的性能？

## 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。