# 线性代数网课代修|机器学习代写 machine learning代考|COMP4702

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• 数值分析
• 高等线性代数
• 矩阵论
• 优化理论
• 线性规划
• 逼近论

## 线性代数作业代写linear algebra代考|MACHINE LEARNING

ML is a statistical technique that analyses structured data mostly used for identifying patterns in the data or predicts its future. In medical terms these patterns can either be used in identifying the risk factors for infection or predicting infected patients in future. This is the most common technology of AI. Structured data is an organized collection of information with some specific defined purpose. In health care databases, the structured data is available in the form of patient details, lab investigation values, demographic data, imaging, genetic and financial information. In health care applications, the ML algorithms attempt to form clusters from patients’ traits or estimate the probability of a particular disease outcome. There exist different ML algorithms like logistic regression and decision trees, etc. to accomplish this task [Jiang et al. 2017].

ML models can be classified into three types for understanding the inputs of data.
(i) The first and most straightforward model is the supervised learning model (SLM). The inputs for this model are labeled, and can be trained to correctly map between the inputs and the labels using features and weights of hidden layers. This model can be exposed to newly recorded data to make predictions after being trained. The accuracy of this model can be measured and refined. This learning can be applied for predictive modeling where relationships can be built taking patient traits as input and the outcomes of interest.
(ii) The second model is unsupervised learning, where there are no user-defined labels. This model has to discover the features on its own from the given inputs in order to perform the mapping with the outputs. This model has much less human intervention and is mostly used for extracting interesting features. The two important learning methods are clustering and principal component analysis (PCA). Clustering, groups the data with similar traits into one cluster and gives cluster labels for the patients. PCA is primarily used for dimensionality reduction, when the traits of the patient are saved with large number of dimensions.

## 线性代数作业代写linear algebra代考|Natural LanguaGe Processing

It comprises several applications like text analysis, speech recognition, translation and many others for making sense of human language. A very large proportion of health care data is generated from narrative text like laboratory reports, medical notes, discharge summaries etc. This data is mostly unstructured and also beyond understandability for any computer program. In this context, the primary goal of NLP is to extract meaningful information from these narrative texts and give assistance in clinical decision-making. In the domain of health care, the important applications of NLP include creating, understanding and classifying clinical documents, analyzing the unstructured medical notes, generating reports for examinations and transcribing patient interactions. Most of the NLP systems learn repeatedly by reabsorbing the previous interaction results as feedback to determine the accurate results and the results that did not meet the required expectations. The two major components of the NLP system are classification and text processing. Text processing is used to identify all the disease-related keywords from the medical notes. A subset of keywords are further selected and their effect on classification is examined for both normal and abnormal cases. After applying classification, a set of validated keywords are generated to improve the structured data and support the process of decision-making [Jiang et al. 2017].

## 线性代数作业代写linear algebra代考|MACHINE LEARNING

ML 是一种分析结构化数据的统计技术，主要用于识别数据中的模式或预测其未来。在医学术语中，这些模式既可以用于识别感染的风险因素，也可以用于预测未来的感染患者。这是最常见的人工智能技术。结构化数据是具有某些特定目的的有组织的信息集合。在医疗保健数据库中，结构化数据以患者详细信息、实验室调查值、人口统计数据、成像、遗传和财务信息的形式提供。在医疗保健应用中，ML 算法尝试根据患者的特征形成集群或估计特定疾病结果的概率。存在不同的 ML 算法，例如逻辑回归和决策树等。完成这项任务 [Jiang et al. 2017]。

(i) 第一个也是最直接的模型是监督学习模型 (SLM)。该模型的输入被标记，并且可以被训练以使用隐藏层的特征和权重在输入和标签之间正确映射。这个模型可以暴露在新记录的数据中，在训练后进行预测。该模型的准确性可以测量和改进。这种学习可以应用于预测建模，其中可以将患者特征作为输入和感兴趣的结果建立关系。
(ii) 第二种模型是无监督学习，没有用户定义的标签。该模型必须从给定的输入中自行发现特征，以便执行与输出的映射。该模型的人工干预少得多，主要用于提取有趣的特征。两种重要的学习方法是聚类和主成分分析（PCA）。聚类，将具有相似特征的数据分组为一个聚类，并为患者提供聚类标签。PCA 主要用于降维，当患者的特征被大量维度保存时。

# 计量经济学代写

## 在这种情况下，如何学好线性代数？如何保证线性代数能获得高分呢？

1.1 mark on book

【重点的误解】划重点不是书上粗体，更不是每个定义，线代概念这么多，很多朋友强迫症似的把每个定义整整齐齐用荧光笔标出来，然后整本书都是重点，那期末怎么复习呀。我认为需要标出的重点为

A. 不懂，或是生涩，或是不熟悉的部分。这点很重要，有的定义浅显，但证明方法很奇怪。我会将晦涩的定义，证明方法标出。在看书时，所有例题将答案遮住，自己做，卡住了就说明不熟悉这个例题的方法，也标出。

B. 老师课上总结或强调的部分。这个没啥好讲的，跟着老师走就对了

C. 你自己做题过程中，发现模糊的知识点

1.2 take note

1.3 understand the relation between definitions