机器学习课程

1. 19 learning_on_the_web.mp4332.96 MB
2. 16 transfer_learning_and_semi_supervised_learning.mp4329.54 MB
3. 1 Introduction_to_ML_and_review_of_linear_algebra__probability__statistics.mp4307.28 MB
4. 15 feature_learning_and_deep_learning.mp4292.79 MB
5. 18 learning_on_images.mp4290.9 MB
6. 2 linear_model.mp4289.19 MB
7. 10 optimization_in_machine_learning.mp4281.58 MB
8. 17 matrix_factorization_and_recommendations.mp4267.69 MB
9. 4 linear_classification.mp4255.5 MB
10. 7 model_combination.mp4252.2 MB
11. 13 introduction_to_graphical_models.mp4249.06 MB
12. 3 overfitting_and_regularization.mp4243.43 MB
13. 12 sparsity_models.mp4237.86 MB
14. 5 basis_expansion_and_kernel_methods.mp4232.25 MB
15. 9 overview_of_learning_theory.mp4227.57 MB
16. 14 structured_learning.mp4213.1 MB
17. 8 boosting_and_bagging.mp4209.73 MB
18. 11 online_learning.mp4207.82 MB
19. 6 model_selection_and_evaluation.mp4135.64 MB
20. 1 Introduction_to_ML_and_review_of_linear_algebra__probability__statistics_(kai).pdf15.51 MB
21. 18 learning_on_images_(kai).pdf12.79 MB
22. 15 feature_learning_and_deep_learning_(kai).pdf11.18 MB
23. 16 transfer_learning_and_semi_supervised_learning_(kai).pdf6.21 MB
24. 17 matrix_factorization_and_recommendations_(kai).pdf3.64 MB
25. 13 introduction_to_graphical_models_(kai).pdf2.17 MB
26. 12 sparsity_models_(tong).pdf1.9 MB
27. 5 basis_expansion_and_kernel_methods_(kai).pdf1.82 MB
28. 4 linear_classification_(kai).pdf1.04 MB
29. 14 structured_learning_(kai).pdf790.95 KB
30. 6 model_selection_and_evaluation_(kai).pdf680.51 KB
31. 2 linear_model_(tong).pdf457.35 KB
32. 19 learning_on_the_web_(tong).pdf181.14 KB
33. 9 overview_of_learning_theory_(tong).pdf164.63 KB
34. 8 boosting_and_bagging_(tong).pdf152.26 KB
35. 7 model_combination_(tong).pdf147.69 KB
36. 10 optimization_in_machine_learning_(tong).pdf139.65 KB
37. 11 online_learning_(tong).pdf134.3 KB
38. 3 overfitting_and_regularization_(tong).pdf129.08 KB