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

 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

 2.1 Overview of data types  2.2 Review of Data pre-processing tools and platforms  2.3 Clean, storage and management of raw data  2.4 Collections of data analysis and data mining

 4.1 The basic concept of association rules  4.2 Low-dimensional binary association rules  4.3 Multi-level association rules  4.4 Multidimensional association rules  4.5 The Affinity analysis based on the association mining

 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

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

◼ Pattern Mining: A Road Map ◼ Pattern Mining in Multi-Level, Multi-Dimensional Space ◼ Constraint-Based Frequent Pattern Mining ◼ Mining High-Dimensional Data and Colossal Patterns ◼ Mining Compressed or Approximate Patterns ◼ Pattern Exploration and Application ◼ Summary

Motivation: Why data mining? What is data mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary

• 大数据的魅力 – 数据挖掘、大数据、大数据案例、大数据的特点 • 大数据时代的思维变革 – 样本和全体、精确性和混杂性、因果关系和相关关系 • 大数据的处理 – 几种主要处理方式、MapReduce编程模型 • 大数据的分析 – 关键技术概述、PageRank初步