• Data Mining – From data warehousing to data mining. – Data pre-processing and data mining life-cycle. – Association and sequence analysis; classification and clustering. – Fuzzy Logic, Neural Networks, and Genetic Algorithms. – Mining Complex Data. • OLAP mining; spatial data mining; text mining; time-series data mining; web mining; visual data mining. • Data warehousing. – Introduction; basic concepts of data warehousing; data warehouse vs. Operational DB; data warehouse and the industry. – Architecture and design; two-tier and three￾tier architecture; star schema and snowflake schema; data capturing, replication, transformation and cleansing. – Data characteristics; metadata; static and dynamic data; derived data. – Data Marts; OLAP; data mining; data warehouse administration

◼ Data Warehouse: Basic Concepts ◼ Data Warehouse Modeling: Data Cube and OLAP ◼ Data Warehouse Design and Usage ◼ Data Warehouse Implementation ◼ Data Generalization by Attribute-Oriented Induction ◼ Summary

Parallel DBMS technologies Proposed in the late eighties Matured over the last two decades Multi-billion dollar industry: Proprietary DBMS Engines intended as Data Warehousing solutions for very large enterprises Hadoop Spark UC Berkeley

线性回归 ●最小二乘法 ●梯度下降 二分类任务 ●对数几率回归-Logistic Regression ●线性判别分析-Linear Discriminate Analysis 多分类任务 ●一对一 ●一对其余 ●多对多 类别不平衡问题

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