### 数学代写|matlab代写|STA518

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

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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

## 数学代写|matlab代写|Deep Learning

Deep learning is a subset of machine learning which is itself a subset of artificial intelligence and statistics. Artificial intelligence research began shortly after World War II [35]. Early work was based on the knowledge of the structure of the brain, propositional logic, and Turing’s theory of computation. Warren McCulloch and Walter Pitts created a mathematical formulation for neural networks based on threshold logic. This allowed neural network research to split into two approaches: one centered on biological processes in the brain and the other on the application of neural networks to artificial intelligence. It was demonstrated that any function could be implemented through a set of such neurons and that a neural net could learn to recognize patterns. In 1948, Norbert Wiener’s book Cybernetics was published which described concepts in control, communications, and statistical signal processing. The next major step in neural networks was Donald Hebb’s book in 1949, The Organization of Behavior, connecting connectivity with learning in the brain. His book became a source of learning and adaptive systems. Marvin Minsky and Dean Edmonds built the first neural computer at Harvard in 1950.

The first computer programs, and the vast majority now, have knowledge built into the code by the programmer. The programmer may make use of vast databases. For example, a model of an aircraft may use multidimensional tables of aerodynamic coefficients. The resulting software, therefore, knows a lot about aircraft, and running simulations of the models may present surprises to the programmer and the users since they may not fully understand the simulation, or may have entered erroneous inputs. Nonetheless, the programmatic relationships between data and algorithms are predetermined by the code.

In machine learning, the relationships between the data are formed by the learning system. Data is input along with the results related to the data. This is the system training. The machine learning system relates the data to the results and comes up with rules that become part of the system. When new data is introduced, it can come up with new results that were not part of the training set.

Deep learning refers to neural networks with more than one layer of neurons. The name “deep learning” implies something more profound, and in the popular literature, it is taken to imply that the learning system is a “deep thinker.” Figure $1.1$ shows a single-layer and multi-layer network. It turns out that multi-layer networks can learn things that single-layer networks cannot. The elements of a network are nodes, where weighted signals are combined and biases added. In a single layer, the inputs are multiplied by weights and then added together at the end, after passing through a threshold function. In a multi-layer or “deep learning” network, the inputs are combined in the second layer before being output. There are more weights and the added connections allow the network to learn and solve more complex problems.

## 数学代写|matlab代写|History of Deep Learning

Minsky wrote the book Perceptrons with Seymour Papert in 1969 , which was an early analysis of artificial neural networks. The book contributed to the movement toward symbolic processing in AI. The book noted that single-layer neurons could not implement some logical functions such as exclusive or (XOR) and implied that multi-layer networks would have the same issue. It was later found that three-layer networks could implement such functions. We give the XOR solution in this book.

Multi-layer neural networks were discovered in the 1960 s but not studied until the 1980 s. In the 1970 s, self-organizing maps using competitive learning were introduced [15]. A resurgence in neural networks happened in the 1980s. Knowledge-based, or “expert,” systems were also introduced in the 1980s. From Jackson [18]
An expert system is a computer program that represents and reasons with knowledge of some specialized subject to solve problems or give advice.
-Peter Jackson, Introduction to Expert Systems
Backpropagation for neural networks, a learning method using gradient descent, was reinvented in the 1980 s leading to renewed progress in this field. Studies began with both human neural networks (i.e., the human brain) and the creation of algorithms for effective computational neural networks. This eventually led to deep learning networks in machine learning applications.

Advances were made in the 1980 s as AI researchers began to apply rigorous mathematical and statistical analysis to develop algorithms. Hidden Markov Models were applied to speech. A Hidden Markov Model is a model with unobserved (i.e., hidden) states. Combined with massive databases, they have resulted in vastly more robust speech recognition. Machine translation has also improved. Data mining, the first form of machine learning as it is known today, was developed.

In the early 1990s, Vladimir Vapnik and coworkers invented a computationally powerful class of supervised learning networks known as support-vector machines (SVM). These networks could solve problems of pattern recognition, regression, and other machine learning problems.

## 数学代写|matlab代写|History of Deep Learning

Minsky 于 1969 年与 Seymour Papert 合着了《感知器》一书，这是对人工神经网络的早期分析。这本书推动了 AI 中符号处理的发展。该书指出，单层神经元无法实现一些逻辑功能，例如异或（XOR），并暗示多层网络也会有同样的问题。后来发现三层网络可以实现这样的功能。我们在本书中给出了 XOR 的解决方案。

-Peter Jackson，专家系统简介

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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。