# 线性代数网课代修|机器学习代写 machine learning代考|CRN33071

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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|AI FOR DIAGNOSIS AND TREATMENT

Diagnosis of disease and suggestion for treatment has been a focus of AI for the past five decades. In 1972 Stanford University in California started working on MYCIN, an early AI program to treat blood infections. MYCIN made an attempt to diagnose patients on the basis of the symptoms reported and results of medical tests. This program also suggests extra laboratory tests and gives a probable diagnosis. After diagnosing the disease, this program suggests a suitable treatment explaining the reasons leading to the diagnosis and suggestion of treatment. This program was found to operate at the same level of proficiency as medical specialists to identify and treat blood infections [Copeland: 2018]. Practically, MYCIN was never used in medicine due to legal and ethical issues not because of its performance weakness. In the mid-1980s, another medical expert system named CADUCEUS was launched by the University of Pittsburgh, which worked on inference engine similar to MYCIN and was intended to improve MYCIN and focus on blood-borne infectious bacteria. The CADUCEUS system could diagnose nearly 1000 different diseases [Banks 1986]. Several such AI medical systems evolved subsequently, out of which some gained importance and a few were not clinically practiced.

## 线性代数作业代写linear algebra代考|ONCOLOGY

Oncology is the field of medicine that diagnoses and treats cancer. AI contributes in resolving several biomedical problems. DL, a subset of AI, extracts features automatically and is more flexible to be applied in different research areas including cancer. It plays a very important role especially in the early detection of cancer. It was stated by Jeremy Howard, the CEO and founder of Enlitic that “If cancer can be detected early, then the probability of survival of the patients is 10 times higher.” DL achieved highest accuracy in early diagnosis compared to many domain experts. The current DL application in oncology includes detecting cancer from gene expression data, which is considered as very complex data due to its high dimensionality. DL extracts meaningful features from this data and classifies the breast cancer cells. This technology also extracts genes helpful for cancer prediction and cancer biomarkers, to detect breast cancer.

DL can also be used for segmenting brain tumors in MR images, with much more stable results compared to segmenting brain tumors manually by physicians, since it is prone to errors due to motion and vision. This can also be used to measure tumor sizes during treatment and also detect new metastases if there are any that may have been overlooked. The algorithm is made to read more CT and MRI scans of patients to get more accurate results. Prognosis gives an approximation of how advanced the cancer is and what the chances of survival are. DL develops a prediction model for prognosis detection of patients suffering from cancer and receiving treatment. It was found that the survival predictive power of $\mathrm{DL}$ is superior to other prediction models [Ali 2019].

## 线性代数作业代写linear algebra代考|ONCOLOGY

DL 还可用于在 MR 图像中分割脑肿瘤，与医生手动分割脑肿瘤相比，结果稳定得多，因为它容易因运动和视觉而出错。这也可用于在治疗期间测量肿瘤大小，如果有任何可能被忽视的情况，还可以检测新的转移灶。该算法用于读取更多患者的 CT 和 MRI 扫描，以获得更准确的结果。预后给出了癌症进展程度和生存机会的近似值。DL 开发了一个预测模型，用于检测患有癌症和接受治疗的患者的预后。发现生存预测能力D大号优于其他预测模型 [Ali 2019]。

# 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