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# CS代写|机器学习代写Machine Learning代考|ACDL2022 Terminology

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## CS代写|机器学习代写Machine Learning代考|Terminology

To conduct machine learning, we must have data first. Suppose we have collected a set of watermelon records, for example, (color $=$ dark; root $=$ curly; sound $=$ muffled), (color $=$ green; root = curly; sound $=$ dull $),($ color $=$ light $;$ root $=$ straight; sound = crisp),…, where each pair of parentheses encloses one record and “=” means “takes value”.

Collectively, the records form a data set, where each record contains the description of an event or object, e.g., a watermelon. A record, also called an instance or a sample, describes some attributes of the event or object, e.g., the color, root, and sound of a watermelon. These descriptions are often called attributes or features, and their values, such as green and dark, are called attribute values. The space spanned by attributes is called an attribute space, sample space, or input space. For example, if we consider color, root, and sound as three axes, then they span a three-dimensional space describing watermelons, and we can position every watermelon in this space. Since every point in the space corresponds to a position vector, an instance is also called a feature vector.

More generally, let $D=\left{x_1, x_2, \ldots, x_m\right}$ be a data set containing $m$ instances, where each instance is described by $d$ attributes. For example, we use three attributes to describe watermelons. Each instance $\boldsymbol{x}i=\left(x{i 1} ; x_{i 2} ; \ldots ; x_{i d}\right) \in \mathcal{X}$ is a vector in the $d$-dimensional sample space $\mathcal{X}$, where $d$ is called the dimensionality of the instance $\boldsymbol{x}i$, and $x{i j}$ is the value of the $j$ th attribute of the instance $\boldsymbol{x}_i$. For example, at the beginning of this section, the second attribute of the third watermelon takes the value straight.

## CS代写|机器学习代写Machine Learning代考|Hypothesis Space

Induction and deduction are two fundamental tools of scientific reasoning. Induction is the process from specialization to generalization, that is, summarizing specific observations to generalized rules. In contrast, deduction is the process from generalization to specialization, that is, deriving specific cases from basic principles. For example, in axiomatic systems of mathematics, the process of deriving a theorem from a set of axioms is deduction. By contrast, learning from examples is an inductive process, also known as inductive learning.

In a broad sense, inductive learning is almost equivalent to learning from examples. In a narrow sense, inductive learning aims to learn concepts from training data, and hence is also called concept learning or concept formation. The research and applications on concept learning are quite limited because it is usually too hard to learn generalized models with clear semantic meanings, whereas in real-world applications, the learned models are often black boxes that are difficult to interpret. Nevertheless, having a brief idea of concept learning is useful for understanding some basic concepts of machine learning.

The most fundamental form of concept learning is Boolean concept learning, which encodes target concepts as Boolean values 1 or 0 , indicating true or false. Taking the training data in – Table $1.1$ as an example, suppose we want to learn the target concept of ripe, assume that the ripeness of a watermelon entirely depends on its color, root, and sound. In other words, whether a watermelon is ripe or not is determined once we know the values of those three variables. Then, the concepts to be learned could be “ripe is watermelon with color $=X$, root $=Y$, and sound $=Z$ “, or equivalently as the Boolean expression “ripe $\leftrightarrow($ color $=$ ?) $\wedge$ (root $=$ ?) $\wedge$ (sound $=$ ?)”, where the “?” marks are the values to be learned from training data.

## CS代写|机器学习代写Machine Learning代考|假设空间

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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。