《人工智能、机器学习与大数据》课程教学资源（参考文献）Latent Wishart processes for relational kernel learning

《人工智能、机器学习与大数据》课程教学资源（参考文献）Multicategory large margin classification methods - hinge losses vs. coherence functions

《人工智能、机器学习与大数据》课程教学资源（参考文献）Latent Wishart processes for relational kernel learning（讲稿）

《人工智能、机器学习与大数据》课程教学资源（参考文献）Buffered Asynchronous SGD for Byzantine Learning

1 Introduction 2 Unsupervised Hashing 3 Supervised Hashing 4 Ranking-based Hashing 5 Multimodal Hashing 6 Deep Hashing 7 Quantization 8 Conclusion 9 Reference

1 Introduction Problem Definition Existing Methods 2 Scalable Graph Hashing with Feature Transformation Motivation Model and Learning Experiment 3 Conclusion 4 Reference

1 Introduction Problem Definition Existing Methods 2 Isotropic Hashing Model Learning Experiment 3 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 4 Conclusion 5 Reference

1 Introduction Problem Definition Existing Methods Motivation and Contribution 2 Isotropic Hashing Model Learning Experimental Results 3 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 4 Conclusion 5 Reference

《经济数据分析 Analysis of Economic Data》课程教学资源（PPT课件讲稿，英文版）Lesson 11 Regressions Part II

◼ 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