 5.1 Introduction of clustering analysis  5.2 Similarity calculation  5.3 Overview of basic clustering techniques  5.4 Partitioning method  5.5 Hierarchical method  5.6 Clustering based on density and grid  5.7 Clustering based on models  5.8 Outlier analysis

 3.1 Learning Problems  3.2 The least square method (LSM)  3.3 Linear regression analysis  1 Simple Linear Regression  2 Multiple Regression  3 Understanding the Regression Output  4 Coefficient of Determination R2  5 Validating the Regression Model

 1.1 What’s big data?  1.2 Overview of data analysis  1.3 Overview of data mining  1.4 Make requirement for different professional applications

隐马尔科夫模型的定义 HMM模型的三个基本问题 EM算法 前向后向算法 鲍姆-韦尔奇算法 维比特算法

1、量子力学基本回顾 2、复合体系描述 3、量子测量及相关问题 §1.1 态叠加原理 ➢ 波叠加 经典 合成的波中有各种成分 相干性 量子 相干性 新特点 §1.2 矩阵力学基础——力学量和算符 §1.3 量子测量及相关问题 1.3.1量子测量基础 1.3.2广义测量和POVM测量

 机器学习是数据分析与挖掘的一种手段  机器学习是什么？  利用经验改善系统自身的性能  关于T和P，程序对E进行学习  Looking for a function  机器学习发展史  人工智能三阶段：推理期、知识期、学习期  深度学习∈机器学习∈人工智能  机器学习基本术语  回归、分类；模型、学习器；样本、样例……  模型选择：奥卡姆剃刀？NFL？

• Why safety? • Origins of safety concerns • Thermal runaway process: 3 stages • Materials with improved battery safety • Materials to solve problems in 3 stages

• Overview of anode materials • Introduction of graphite • Chemistry of graphite • Molecular structure and Li insertion • Synthesis and modification methods • One literature example

• Lithium-metal batteries • Safety problem • Lithium-ion batteries • Components, working mechanism • Cathode materials: LiFePO4 , Li-Mn-O

Section 1 Introduction of energy on the earth Section II Fundamentals of Fuel Cell Chemical power sources Section III Fundamentals of Solar Cell Physical power sources