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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%                       Title                         %%%%%%%%%%%%%%%%
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\title{机器学习回顾 \& Location实验}
\author{游浩然 }
\institute[\bf HUST]{<EMAIL>}
\medskip
\date{\today}

\begin{document}

\begin{frame}
  \titlepage
\end{frame}

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%%%%%%%%%%%%%                       Content                       %%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%                       Section                       %%%%%%%%%%%%%%%%
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\section{机器学习回顾}
% frame 1
\begin{frame}{机器学习}
\begin{itemize}
  \item 传统机器学习
  \begin{itemize}
    \item 优化问题
    \item 分/聚类问题
    \item 预测问题
  \end{itemize}
  \item 深度学习
  \begin{figure}[!htbp]
    \centering
    \includegraphics[width=8cm]{dl.pdf}
  \end{figure}
\end{itemize}
\end{frame}

\subsection{优化问题}
\begin{frame}{优化问题}
\begin{block}{Example: EV Charger Location for Ireland (ICM 2018)}
\end{block}
\begin{itemize}
  \item destination charging designed for charging for several hours at a time or even overnight.
  \item supercharging designed for longer road trips to provide up to 170 miles of range in as little as 30 minutes of charging.
\end{itemize}
one of five tasks:
\begin{itemize}
  \item Determine the optimal number, placement and distribution of charging stations if your country could migrate all their personal passengers vehicles to all-electric vehicles instantaneously.
  \item Present a proposal for evolving the charging network of your chosen country from zero chargers to a full electric-vehicle system.
  \item Based on your growth plan, what is the timeline you propose for the full evolution to electric vehicles in your chosen country.
\end{itemize}
\end{frame}

\subsection{分/聚类问题}
\begin{frame}{分/聚类问题}
\begin{block}{Example: 对通讯记录进行社群划分 (华中赛 2017)}
\end{block}
\vspace{1cm}
\begin{itemize}
  \item 给定无向图，区分个体差异，实现分群
  \item 给定有向图，刻画任意两点间的相似关系
  \item 给出某营业部近三个月的内部通讯记录，结合并完善问题1，2的数学模型，实现个体的分群。
  \item 考虑通讯的位置，时间，通话频率等多种因素建立综合的数学模型，挖掘更多的信息。
\end{itemize}
\vspace{1cm}
\end{frame}

\subsection{预测问题}
\begin{frame}{预测问题}
\begin{block}{Example: 高炉炼铁-铁水含硅量预测 (MathorCup 2017)}
\end{block}
\vspace{0.3cm}
炼铁过程依时间序列采集的工艺参数是一个高维的时间序列，影响因素数以百计。其终极生产指标产量、能耗、铁水质量等指标都与炼铁过程中的一项控制性中间指标——炉温，即铁水含硅量[Si]密切相关。
\begin{itemize}
  \item 从给定数据表中[Si]-[S]-FL-PML依序号排列的1000炉生产数据中，自主选择学习样本和算法，建立[Si]预测动态数学模型，包括一步预测和两步预测。
  \item 自主选择验证样本，验证预测成功率，包括数值预测成功率和炉温升降方向预测成功率，并讨论其动态预测控制的可行性。
  \item 建立质量指标[S]的优化数学模型，讨论按照优化模型计算结果进行[Si]预测控制的预期效果。
\end{itemize}
\end{frame}

%%%%%%%%%%%%%%%%

\section{Location Assignment}
\subsection{Method}
\begin{frame}{KNN}
main-idea:
\begin{itemize}
  \item 用K个最近邻样本输出的平均值作为回归预测值。
\end{itemize}
\vspace{1cm}
feature selection:
\begin{itemize}
  \item 取训练集和测试集feature的交集，总特征数为423
  \item 将信号强度0变为极弱信号强度-100
  \item 按比例划分训练集和交叉验证集。
\end{itemize}
\end{frame}

\subsection{Result}
\begin{frame}{Distribution}
\begin{figure}[!htbp]
  \centering
  \includegraphics[width=12cm]{distribution.png}
  \caption{Distribution}
\end{figure}
\end{frame}

\begin{frame}{CDF}
\begin{figure}
  \centering
  \includegraphics[width=8cm]{cdf.png}
  \caption{Cumulative Distribution Function}
\end{figure}
\end{frame}

\begin{frame}
\begin{block}{Thank You.}
\end{block}
\end{frame}

\end{document}
