4.1 Introduction 4.2 Discrete Probability Distributions 4.3 Continuous Probability Distributions 4.4 Conclusion

3.1 Random Variables 3.2 Cumulative Distribution Function 3.3 Discrete Random Variables(DRV) 3.4 Continuous Random Variables 3.5 Functions of a Random Variable 3.6 Mathematical Expectation 3.7 Moments 3.8 Quantiles 3.9 Moment Generating Function (MGF) 3.10 Characteristic Function 3.11 Conclusion

香港中文大学：《Probability and Statistics for Engineers》课程教学资源（辅导材料）Tutorial 4：Discrete Random Variables 2

香港中文大学：《Probability and Statistics for Engineers》课程教学资源（辅导材料）Tutorial 3：Discrete Random Variables 1

香港中文大学：《Probability and Statistics for Engineers》课程教学资源（课件讲稿）Discrete random variables

6.0 Introduction 6.1 Block Diagram Representation of Linear Constant-Coefficient Difference Equations 6.2 Signal Flow Graph Representation of Linear Constant-Coefficient Difference Equations 6.3 Basic Structures for IIR Systems 6.4 Transposed Forms 6.5 Basic Network Structures for FIR Systems

《人工智能、机器学习与大数据》课程教学资源（参考文献）Column Sampling based Discrete Supervised Hashing

《人工智能、机器学习与大数据》课程教学资源（参考文献）Discrete Latent Factor Model for Cross-Modal Hashing

《人工智能、机器学习与大数据》课程教学资源（参考文献）Deep Discrete Supervised Hashing

1 Introduction 2 Learning to Hash Isotropic Hashing Scalable Graph Hashing with Feature Transformation Supervised Hashing with Latent Factor Models Column Sampling based Discrete Supervised Hashing Deep Supervised Hashing with Pairwise Labels Supervised Multimodal Hashing with SCM Multiple-Bit Quantization 3 Distributed Learning Coupled Group Lasso for Web-Scale CTR Prediction Distributed Power-Law Graph Computing 4 Stochastic Learning Fast Asynchronous Parallel Stochastic Gradient Descent Distributed Stochastic ADMM for Matrix Factorization 5 Conclusion