| 1. | |
Lecture1. Introduction | Basic first-order methods |  |
| 2. | |
Lecture2. Background | Background in Machine Learning | |
| 3. | |
Lecture3. Gradient descent | Gradient descent/Subgradient/ Convex | |
| 4. | |
Lecture4. Subgradient Method | Gradient vs. Subgradient | |
| 5. | |
Lecture5. SGD | Stochastic gradient descent | |
| 6. | |
Lecture6. Pegasos SVM solver | Supprot Vector Machines | |
| 7. | |
Lecture7-1. Proximal Gradient Method-1 | Proximal Gradient Method | |
| 8. | |
Lecture7-2. Proximal Gradient Method-1 | Proximal Gradient Method | |
| 9. | |
Lecture8. Accelerated First Order Method | Acceleration | |
| 10. | |
Lecture9. ADMM | Alternating direction method of multipliers | |
| 11. | |
Lecture10. Duality1 | Dual Problem | |
| 12. | |
Lecture11. Duality2 | Optimality/ Duality | |
대구광역시 동구 동내로 64(동내동 1119) 우)41061
COPYRIGHT keris. all rights reserved
페이스북
트위터
카카오톡
네이버밴드
링크복사
