《贝叶斯学习与随机矩阵及在无线通信中的应用 BI-RM-AWC》课程教学资源（学习资料）Random Matrix Theory and Wireless Communications

• 2.1 Introduction • 2.2 Polynomial Curve Fitting: A Toy • 2.3 Over-fitting • 2.4 Cross-Validation • 2.5 Regularization • 2.6 Linear Basis Function Models • 2.7 Least Squares Revisited • 2.8 Bayesian Linear Regression

6.1 Non-asymptotic Analysis: Concentration inequality 6.2 Non-asymptotic Analysis: extreme singular values

1. Continuing Mapping Theorem 2. Silverstein Theorem

电子科技大学：《贝叶斯学习与随机矩阵及在无线通信中的应用 BI-RM-AWC》课程教学资源（课件讲稿）04 Asymptotic Spectrum Theorems（2/4）

电子科技大学：《贝叶斯学习与随机矩阵及在无线通信中的应用 BI-RM-AWC》课程教学资源（课件讲稿）03 Transforms

1.1 Wireless Channels 1.2 Single Antenna Channels 1.3 Multiple Antenna Channels 1.4 Introduction of Random Matrix

3.1 极大似然 3.2 隐变量 3.3 EM算法 3.4 混合高斯模型中的EM思想 3.5 论文举例

Section 1 Introduction Section 2 Random Matrix Theory Section 3 Applications to Wireless Communications Section 4 Appendices

1 Introduction 2 Probability Distributions 3 Linear Models for Regression 4 Linear Models for Classification 5 Neural Networks 6 Kernel Methods 7 Sparse Kernel Machines 8 Graphical Models 9 Mixture Models and EM 10 Approximate Inference 11 Sampling Methods 12 Continuous Latent Variables 13 Sequential Data 14 Combining Models