 6.1 Basic Concepts  6.2 Decision Tree Induction  6.3 Bayes Classification Methods  6.4 Rule-Based Classification  6.5 Model Evaluation and Selection  6.6 Techniques to Improve Classification Accuracy

电子科技大学：《数据分析与数据挖掘 Data Analysis and Data Mining》课程教学资源（课件讲稿）Lecture 03 Regression Analysis（Logistic Regression）

电子科技大学：《数据分析与数据挖掘 Data Analysis and Data Mining》课程教学资源（课件讲稿）量子机器学习（量子K-means算法）

1、聚类算法简介 2、K-means算法 3、DBSCAN算法 4、层次聚类算法

电子科技大学：《算法设计与分析 Design and Analysis of Algorithms》研究生课程教学资源（课件讲稿，英文版）Stable Matching

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5.1 Load Balancing 5.2 Center Selection 5.3 The Pricing Method: Vertex Cover 5.4 LP Rounding: Vertex Cover * 5.5 Load Balancing Reloaded 5.6 Knapsack Problem

4.1 Polynomial-Time Reductions 4.2 Reductions via \Gadgets\ 4.3 Definition of NP 4.4 NP-Completeness 4.5 Sequencing Problems 4.6 Partitioning Problems 4.7 Graph Coloring 4.8 Numerical Problems

2.1 Analysis 2.2 Asymptotic Order of Growth(渐进分析) 2.3 Three Techniques for Designing Algorithms 1. Greedy Algorithms 2. Divide and Conquer 3. Dynamical Programming

NR概述 NR的网络架构 NR的基本概念 NR的物理信道