assignmentutor™您的专属作业导师

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
• 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

## 机器学习代写|机器学习代写machine learning代考|Definition and Examples of Supervised Learning

Supervised learning can be defined as the process of learning a function that maps an input to an output based on teaching the statistical machine learning method with input-output pairs. The training data consist of pairs of objects (usually vectors): one component of the pair is the input data (predictors $=$ explanatory variable $=$ input) and the other, the desired results (response variable $=$ dependent variable $=$ output). The output of the function can be a numerical value (as in regression problems) or a class label (as in multinomial regression). The goal of supervised learning is to learn a function that, given a sample of data and desired outputs, best approximates the relationship between input and output observable in the data. This function should be capable of predicting the value corresponding to any valid input object after having seen a series of examples of training data. Under optimal conditions, the algorithm correctly determines the class labels for unseen instances. This implies a learning algorithm that is able to generalize from the training data to unseen situations in a “reasonable” way.

Suppose you’re teaching your child to distinguish between corn and tomato (Fig. 1.5). First you show him (her) a picture of an ear of corn and a picture of a tomato. In the learning process, your child must keep in mind that if the color is yellow and the shape is not round, then it is probably an ear of corn, but if the color is red and the shape is round, then it is probably a tomato. This is how your child learns. Then you can show a third picture and ask your child to classify the vegetable as either ear of corn or tomato. When you show the third picture, he (she) will very likely identify if the vegetable is ear of corn or tomato, due to the fact that we have already labeled the two pictures into categories, so your child knows what is an ear of corn and what is a tomato. This example illustrates how supervised learning works using ground truth data that consist of having prior knowledge of what the output values of our samples should be.

To give another example, imagine that you’re training a seal to applaud (Fig. 1.6). The goal is to make the seal applaud when you raise your right hand. The training process consists of presenting the seal with enough examples by raising your right hand and rewarding it with some great candy whenever it applauds when it sees your right hand is raised. In the same way, the seal may be “punished” if it applauds whenever your right hand is not raised, by doing something unpleasant for the seal but not harmful. Supervision involves stimulating the seal to respond to positive samples by rewarding it, and not to respond to negative samples by “punishing” it. Hopefully, the seal then obtains a built-in feeling (hypothesis) for applauding whenever you raise your hand right. The process is evaluated by presenting the seal with another person raising his/her right hand, someone who did not take part in the training process and who is unknown to the seal. However, based on its built-in feeling for what a person with his/her raised right hand looks like, the seal should be able to transfer this knowledge to the present person. It must then consider and decide whether or not it wants to signal the presence of a right hand raised by applauding.

## 机器学习代写|机器学习代写machine learning代考|Definitions and Examples of Unsupervised Learning

Unsupervised learning is when you only have input (predictors $=$ independent variables) data $(X)$ and no previous knowledge of corresponding labeled outputs or response variables (Fig. 1.8). So its goal is to deduce the natural structure present within a set of data points. In other words, to extract the underlying structure or distribution in the data in order to learn more about the data, that is, the network uses training patterns to discover emerging collective properties and organizes the data into clusters. In unsupervised learning (unlike supervised learning), there is no correct answer (output $=$ response variable $=$ dependent variable) and there is no teacher. For this reason, we are not interested in prediction since we do not have an associated response variable $Y$. Statistical machine learning algorithms under unsupervised learning are left to their own devices to discover and present the interesting structure in the data. However, there is no way to determine if our work is correct since we don’t know the right answer because the job was done without supervision. Unsupervised learning problems can be divided into clustering and association problems.

Clustering: A clustering problem is when you want to discover the inherent groupings in the data, such as grouping maize hybrids by their genetic architecture. Another example is grouping people according to their consumption behaviors. But in both cases we cannot check if the classifications are correct since we don’t know the true grouping of each individual.

# 机器学习代考

.

. 机器学习代写|机器学习代写

## 有限元方法代写

assignmentutor™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。

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