| Weekly Schedule and Class Notes |
- Lecture notes : all notes will be posted in this section.
- It is your responsibility to download, print and bring the notes to the class. Notes will be available 24 hours before each class.
|
Week |
Date |
Topic |
Reading |
Assignments |
Notices and Dues |
Notes |
|
1 |
09/05 (09/06) |
1. Pre-introduction 2. System, Processes and Network |
Lecture Note |
Homework #1 (Homepage) |
||
|
2 |
09/12 (09/13) |
3. Stable Network Vs Unstable Network |
Lecture Note |
|
Due for homework #1 |
|
|
3 |
09/19 (09/20) |
4. Shortest Path in Stable Network 5. Complexity of Stable Network |
Lecture Note |
Homework #2 (Network modeling) |
Homework #2 (Network modeling) |
|
|
4 |
09/26 (09/27) |
6. Embeded system programming architecture 7. Programming (ligprog, intlinprog) 8. Maximal flow problem , Multi-product maximal flow problem |
Lecture Note |
Homework #3 (Multi-product Maximal Flow problem) |
Homework #3 |
|
|
5 |
10/03 (10/04) |
9. Graph & Tree 10. Minimal Spanning Tree 11. Kruskel algorithm and Prim's algorithm 12. Implementation of Prim's algorithm |
Lecture Note |
[Dataset] |
||
|
6 |
10/10 (10/11) |
13. Local Search and Heuristics (Steepest Descent method) 14. Nonlinear function (Rosenbrock logarithmic banana function) |
Lecture Note |
|||
|
7 |
10/17 (10/18) |
Reading days |
Lecture Note |
|||
|
8 |
10/24 (10/25) |
15. Homogeneous system Vs. Non-homogeneous system 16. Handling of Constraints (Primal problem Vs Dual Problem) 17. KKT Condition for NLP |
Lecture Note |
|||
|
9 |
10/31 (11/01) |
16. Metaheuristics 17. Simulated Annealing |
Lecture Note |
|||
|
10 |
11/07 (11/08) |
midterm |
Lecture Note |
|||
|
11 |
11/14 (11/15) |
18. Histroy of Metaheuristics 19. Deep Learning : First Introduction |
Lecture Note |
Homework #4 (Data for deep learning) |
Due for homework #4 |
|
|
12 |
11/21 (11/22) |
20. Deep Learning 21. Seminconduct Design |
Lecture Note |
|||
|
13 |
11/28 (11/29) |
22. Backpropagation 23. Bias in Deep neural network |
Lecture Note |
|||
|
14 |
12/05 (12/06) |
24. Deep Learning implementation 25. Data regularization and another activation function |
Lecture Note |
Homework #5 (Deep learning-based data prediction) |
Due for homework #5 |
|
|
15 |
12/12 (12/13) |
26. Various acitivation fuctions (Sigmoid, Tanh, ReLU, softmax) 27. Training options (SGMD, Momentum, Adagrad, RMSProp, Adadelta, ADAM) |
Lecture Note |
|||
| 16 |
12/19(12/20) |
Final exam |
Lecture Note |