### 统计代写|机器学习作业代写Machine Learning代考|Machine Learning Basics

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• 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

## 统计代写|机器学习作业代写Machine Learning代考|Supervised learning

Supervised learning needs labeled data for training. A training example is called a data point or instance and consists of an input and output pair $(x, y) . y$ is the output or ground truth for input $x$. Contrary to supervised learning, in unsupervised learning, the data only provides inputs. A multiset of training examples forms the training data set. The training data set is also called gold standard data and is as close to the ground truth as possible. The training set is used to produce a predictor function $f$, also called decision function, that maps inputs $x$ to $y=f(x)$. The goal is to produce a predictor $f$ that works with examples other than the training examples. In other words, $f$ needs to generalize beyond the training data and provide accurate predictions on new, unseen data. $f$ provides an approximation on unseen data. The input $x$ is a feature vector or covariates, the output $y$ is the response. A feature vector $\phi(x)$ is a map of feature names to feature values, i.e., strings to doubles. A feature vector $\phi(x) \in \mathbb{R}$ is a real vector $\phi(x)=\left[\phi_{1}(x), \phi_{2}(x), \ldots \phi_{n}(x)\right]$, where each component $\phi_{i}(x)$ with $i=1, \ldots n$ represents a feature. The feature vector is computed by the feature extractor $\phi$ and can be thought of as a point in a high-dimensional feature space. A feature in a feature vector can be weighted, which means, not every feature necessarily contributes equally to a prediction. A weight is a real number that is multiplied with the feature value. The weights are represented in a separate weight vector because we want one single predictor that works on any input. Given a feature vector $\phi(x)$ and a weight vector $w$, we calculate the prediction score as the inner product of the vectors $w \cdot \phi(x)$. We do

not know the weights in vector $w$ beforehand, they are learned during training.

To predict, for instance, if a news article is about politics or sports, we need to find out what properties of input $x$ might be relevant to predict if the article belongs to the category “politics” or “sports”. This process is called feature extraction. There are machine learning techniques where no function is generated. For example, decision tree induction creates a decision tree, as the name suggests, usually represented as a dendrogram and not a function. To begin with, we will start with a function and explain other techniques in later chapters.

If we try to classify the data into categories, e.g., medical images into healthy or pathologic tissue, we have a classification problem. Classification has discrete outputs such as “true” or “false” or “0” or ” 1 “. If there are just two categories, it is a binary classification problem. Often, the outputs are probabilities, e.g., a mail is spam with 80 percent probability. In other words, the dichotomy of the output is not imperative.

If there are more categories, e.g., restaurant reviews using a Likert scale, as shown in Figure 2.2, it is a multiclass classification problem.

## 统计代写|机器学习作业代写Machine Learning代考|Perceptron

The perceptron is a simple neural network, proposed in 1958 by Rosenblatt, which became a landmark in early machine learning history [10]. It is one of the oldest machine learning algorithms. The perceptron consists of a single neuron and is a binary classifier that can linearly separate an input $x$, a real valued vector, into a single, binary output. It is an online learning algorithm. Online learning means it processes one input at a time. The data is presented in a sequential order and updates the predictor at each step using the best predictor. The perceptron is

modeled after a neuron in the brain and is the basic element of a neural network.

There are different types of perceptrons. They can have one or more input and output links. The input links correspond to the dendrites of a real neuron, the output links to the synapses. Also, there are different types of activation functions. The perceptron shown in Figure $2.3$ consists of three weighted input links, a sum function, a step activation function and one output link.
A perceptron performs three processing steps:

1. Each input signal $x$ is multiplied by a weight $w: w_{i} x_{i}$
2. All signals are summed up
3. The summed up inputs are fed to an activation function
The output is calculated using the output function $f(x)$, as defined by equation 2.3.
$$f(x)=\sum_{i=1} w_{i} x_{i}+b$$
where
\begin{aligned} w &=\text { Weight } \ x &=\text { Input } \ b &=\text { Bias } \end{aligned}

## 统计代写|机器学习作业代写Machine Learning代考| Unsupervised learning

In supervised learning, we have input and output data. In unsupervised learning, we do not have labeled training data. In other words, we only

have input data, no output data, i.e., there is no $y$. Instead of predicting or approximating a variable, we try to group the data into different classes. For instance, we have a population of online shop users that we try to segment into different types of buyers. The types could be social shopper, lifestyle junkie and detached introvert. In this case, we have three clusters. If an observation point can only belong to one cluster, it is an exclusive cluster, if it is allowed to be part of more than one cluster, it is a non-exclusive cluster, in other words, the clusters can be overlapping.

Let $N$ be a set of unlabeled instances $D=x_{1}, x_{2}, \ldots, x_{N}$ in a $d$ dimensional feature space, in clustering $D$ is partitioned into a number of disjoint subsets $D_{j} \mathrm{~s}$ :
$$D=\cup_{j=1}^{k} D_{j}$$
where
\begin{aligned} &D_{i} \cap D_{j}=\emptyset \ &i \neq j \end{aligned}
The points in each subset $D_{j}$ are similar to each other according to a given criterion $\varnothing$. Similarity is usually expressed by some distance measure, such as the Euclidean distance or Manhattan distance.

Cluster analysis can be used for understanding the underlying structure of a dataset by grouping it into classes based on similarities. Grouping similar objects into conceptually meaningful classes has many reallife applications. For instance, biologists spent a considerable amount of time on grouping plants and animals into classes, such as rodents, canines, felines, ruminants, marsupials, etc. Clustering is also used for information retrieval, for instance, by grouping Web search results into categories, such as news, social media, blogs, forums, marketing, etc. Each category can then be divided into subcategories. For example, news is divided into politics, economics, sports, etc. This process is called hierarchical clustering. Humans, even young children, are skilled at grouping objects into their corresponding classes. This capability is important to humans for understanding and describing the world around us. This is crucial for our survival. We need to know which groups of animals are harmful or which traffic situations are potentially dangerous.

## 统计代写|机器学习作业代写Machine Learning代考|Perceptron

1. 每个输入信号X乘以权重在:在一世X一世
2. 汇总所有信号
3. 总和的输入被馈送到激活函数
使用输出函数计算输出F(X)，如公式 2.3 所定义。
F(X)=∑一世=1在一世X一世+b
在哪里
在= 重量  X= 输入  b= 偏见

D=∪j=1到Dj

D一世∩Dj=∅ 一世≠j

## 广义线性模型代考

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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。