## 计算机代写|机器学习代写machine learning代考|CS446

2022年10月8日

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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
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## 计算机代写|机器学习代写machine learning代考|Nonlinear Regression

So far, we have limited our discussion to models of the form $y=X \theta$, mostly because these offered us a convenient (closed form) solution to finding lines of best fit in terms of $\theta$.

However, this type of model has several limitations that we might wish to overcome, such as:
14 The derivative of Equation (2.54) is more obvious after expanding $x_i \cdot \theta=\sum_{k=1}^K x_{i k} \theta_k$.

• We cannot incorporate simple constraints on our parameters, such as that a certain parameter should be positive, or that one parameter is larger than another (which might be based on domain knowledge of a certain problem).
• Although we can manually engineer nonlinear transforms of our features (as we did in sec. 2.3.1), we cannot have the model learn these nonlinear relationships automatically.
• The model cannot learn complex interactions among features, for example, that length is correlated with ratings, but only if the user is female. ${ }^{15}$

These goals can potentially be realized if we are allowed to transform model parameters: for instance, we could ensure that a particular parameter was always positive by fitting
$$\theta_k=\log \left(1+e^{\theta_k^\theta}\right)$$
(this is known as a ‘softplus’ function; note that this function smoothly maps $\theta_k^{\prime} \in \mathbb{R}$ to $\theta_k \in(0, \infty)$ ); or if we wanted one feature to be larger than another (e.g., $\theta_k>\theta_j$ ) we could simply add the positive quantity above to another feature:
$$\theta_k=\theta_j+\log \left(1+e^{\theta_k}\right) .$$
Roughly speaking, fitting these types of nonlinear models (and especially models that deal with complex combinations of parameters) is the basic goal of deep learning. We will see various examples of nonlinear models in later chapters, including models based on deep learning (e.g., secs. $7.6$ and 9.4). In Chapter 3 (sec. 3.4.4) we present the basic approach used to fit these types of models using high-level optimization libraries.

## 计算机代写|机器学习代写machine learning代考|Image Popularity on Reddit

Lakkaraju et al. (2013) used regresssion algorithms to estimate thé successs of content (e.g., number of upvotes) on reddit. Other than building an accurate predictor, their main goal is to understand and disentangle which features are most influential in determining content popularity. Presumably, one of the biggest predictors of success is the quality of the content itself. Predicting whether a submission is of high quality (e.g., whether an

image is funny or aesthetically attractive) is presumably incredibly challenging. To control for this high-variance factor of content quality, Lakkaraju et al. (2013) study resubmissions, that is, content (images) that has been submitted multiple times. This way, if one submission is more successful than another (of the same image), the difference in success cannot be attributed to the content itself, and must arise due to other factors such as the title of the submission or the community it was submitted to.

Having controlled for the effect of the content itself, the goal is then to distinguish between features that capture the specific dynamics of reddit itself, versus those that arise due to the choice of title (i.e., how the content is ‘marketed’). Various features are extracted that model reddit’s community dynamics, such as the following:

• One of the largest predictors of successful content is simply whether it has been submitted before (as we saw in Figure 2.13, which is based on the same dataset); this is captured via an exponentially decaying function.
• However, the above effect might be mitigated if enough time has passed between resubmissions (by when the original submission is forgotten, or the community has enough new users); this is captured using a feature based on the inverse of the time delta between submissions.
• Resubmissions might still be successful if they are resubmitted to largely non-overlapping communities (subreddits).
• Submission success may correlate with the time of day. For example, submissions may be most successful during the highest-traffic times of day, or alternately they may be more successful if submitted when there is less competition.

# 机器学习代考

## 计算机代写|机器学习代写machine learning代考|非线性回归

$$\theta_k=\log \left(1+e^{\theta_k^\theta}\right)$$

$$\theta_k=\theta_j+\log \left(1+e^{\theta_k}\right) .$$

## 计算机代写|机器学习代写machine learning代考|图片在Reddit上的受欢迎程度

Lakkaraju等人(2013)使用回归算法来估计reddit上thé成功的内容(例如，点赞数)。除了建立一个准确的预测器之外，他们的主要目标是理解和理清哪些特征对决定内容流行程度最有影响。据推测，成功的最大预测因素之一是内容本身的质量。预测一个提交是否高质量(例如，

## 有限元方法代写

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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。