[NLP#1] Algorithms and Machine Learning Basics

1. 时间复杂度,空间复杂度分析

2. Master’s Theorem,递归复杂度分析

3. 动态规划以及Dynamic Time Warpping

4. Earth Mover’s Distance

5. 维特比算法

6. LR、决策树、随机森林、XGBoost

7. 梯度下降法、随机梯度下降法、牛顿法

8. Projected Gradient Descent

9. L0, L1, L2, L-Infinity Norm

10. Grid Search, Bayesian Optimization

11. 凸函数、凸集、Duality、KKT条件

12. Linear SVM、Dual of SVM

13. Kernel Trick, Mercer’s Theorem

14. Kernelized Linear Regression、Kernelized KNN

15. Linear/Quadratic Programming

16. Integer/Semi-definite Programming

17. NP-completeness/NP-hard/P/NP

18. Constrained Relaxation、Approximate Algorithm

19. Convergence Analysis of Iterative Algorithm