TA release homework3.

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%%
%% This is file `iidef.sty',
%% generated with the docstrip utility.
%%
%% The original source files were:
%%
%% thucoursework.dtx (with options: `sty')
%%
%% This is a generated file.
%%
%% Copyright (C) 2021 by zhaofeng-shu33 <616545598@qq.com>
%%
%% This work may be distributed and/or modified under the
%% conditions of the LaTeX Project Public License, either version 1.3
%% of this license or (at your option) any later version.
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[2020/09/09 2.6 Tsinghua University Coursework Template]
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%%************************************************************************************

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% Homework Template
\documentclass[a4paper]{article}
\usepackage{ctex}
\usepackage{amsmath, amssymb, amsthm}
\usepackage{moreenum}
\usepackage{mathtools}
\usepackage{url}
\usepackage{bm}
\usepackage{enumitem}
\usepackage{graphicx}
\usepackage{subcaption}
\usepackage{booktabs} % toprule
\usepackage[mathcal]{eucal}
\usepackage[thehwcnt = 3]{iidef}
\thecourseinstitute{清华大学电子工程系}
\thecoursename{\textbf{媒体与认知}}
\theterm{2023-2024学年春季学期}
\hwname{作业}
\begin{document}
\courseheader
% 请在YOUR NAME处填写自己的姓名
\name{YOUR NAME}
\vspace{3mm}
\centerline{\textbf{\Large{理论部分}}}
\section{单选题15分}
% 请在?处填写答案
\subsection{\underline{?}}
\subsection{\underline{?}}
\subsection{\underline{?}}
\subsection{\underline{?}}
\subsection{\underline{?}}
\section{计算题15 分)}
\subsection{给定两个类别的样本分别为:
\begin{align*}
&\omega_1:\{(3,1),(2,2),(4,3),(3,2)\} \\
&\omega_2:\{(1,3),(1,2),(-1,1),(-1,2)\}
\end{align*}
试利用LDA将样本特征维数压缩为一维。
}
\vspace{3mm}
\subsection{模型训练通常需要大量的数据假设某采集的数据集包含80\%的有效数据和20\%的无效数据。采用一种算法判断数据是否有效其中无效数据被成功判别为无效数据的概率为90\%而有效数据被误判为无效数据的概率为5\%。如果某条数据经过该算法被判别为无效数据,则根据贝叶斯定理,这条数据是无效数据的概率是多少?(提示:全概率公式$P(Y)=\sum^{N}_{i=1}P(Y|X_i)P(X_i)$)\\}
\vspace{3mm}
\subsection{设有两类正态分布的样本集,第一类均值为$\mu_1=[2,-1]^T$,第二类均值为$\mu_2=[1,1]^T$。两类样本集的协方差矩阵和出现的先验概率都相等:$\Sigma_1=\Sigma_2=\Sigma=\left[ \begin{array}{cc}
4 & 2 \\
2 & \frac{4}{3}
\end{array} \right]$$p(\omega_1)=p(\omega_2)$。试计算分类界面,并对特征向量$x=[6,2]^T$分类。}
\vspace{3mm}
\subsection{给定异或的样本集$D=\left\{\left((0,0)^T,-1\right),\left((0,1)^T,1\right),\left((1,0)^T,1\right),\left((1,1)^T,-1\right)\right\}$该样本集是线性不可分的,可采用如下所示的多项式函数$\phi(\mathbf{x})$将样本$D=\left\{(\mathbf{x}_n,y_n)\right\}$映射为$D_\phi=\left\{(\phi(\mathbf{x}_n),y_n)\right\}$,其中$\phi(\mathbf{x})$满足
\begin{equation*}
\begin{aligned}
\phi_1(\mathbf{x})&=2(x_1-0.5) \\
\phi_2(\mathbf{x})&=4(x_1-0.5)(x_2-0.5)
\end{aligned}
\end{equation*}
\\
\qquad(1) 给出映射后的样本集;\\
\qquad(2) 在映射后的样本集中设计一个线性SVM分类器给出支持向量及分类界面。
}
\vspace{3mm}
\subsection{使用KMeans算法对2维空间中的6个点$(0,2)$,$(2,0)$,$(2,3)$,$(3,2)$,$(4,0)$,$(5,4)$进行聚类,距离函数选择欧氏距离$d=\sqrt{(x_1-x_2)^2+(y_1-y_2)^2}$\\
\qquad (1)起始聚类中心选择(0,0)和(4,3),计算聚类中心;\\
\qquad (2)起始聚类中心选择(1,4)和(3,1),计算聚类中心。\\
}
\vspace{3mm}
\centerline{\textbf{\Large{编程部分}}}
\vspace{3mm}
% 请根据是否选择自选课题的情况选择“编程作业报告”或“自选课题进度汇报”中的一项完成
\section{编程作业报告}
% 请在此处完成编程作业报告
\section{自选课题进度汇报}
% 请在此处介绍自选课题
\end{document}
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