| 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 |
09/04 |
1. Course Organization /Introduction |
Lecture Note |
|
|
|
|
2 |
09/11 |
2. Data Acquistion protocols and framework 3. Types of Mathematical programming 4. Data format (I) : JSON |
Lecture Note |
[Homework #1 : Your own Homepage] [Homework #2: Matlab & Python] [Homework #3: Node-red] |
[All homeworks's due is on Sep 17th, Midnight] |
- Sufficient & Necessary Condition - TCP/ MQTT - Edge Computing - Open API |
|
3 |
09/18 |
5.Ordinary Network & Probabilistic Network 6. Rank and analysis of Linear Programming 7. Python test for "Nonlinear Programming" 8. Node-red Test for "REST API" & "Json Format" |
Lecture Note |
[Homework #4 : Nonlinear Programming with Python]
[Homework #5 : Node-red with Json format] |
[All homeworks's due is on Sep 24th, Midnight] |
|
|
4 |
09/25 |
9.Types of Network problems (Shortest path, maximal flow) 10. Basic variable and Nonbasic variable in Linear programming 11. Multivariate unconstraint programming 12. Steep Descent Gradient method 13. Moment & ADAM |
Lecture Note |
|
||
|
5 |
10/05 |
14. Nonlinear unconstraint optimization 15. Nonlinear constraint optimization 16. primal problem Vs Dual problem 17. Heuristics and local search |
Lecture Note |
- Strong duality - Week duality |
||
|
6 |
10/09 |
18. Metaheuristics 19. Genetric algorithm |
Lecture Note |
|
||
| 7 |
10/23 |
20. Function call : Varargin, Varargout 21. Json format |
Lecture Note |
|||
|
8 |
10/30 |
Mid-Term |
|
|||
|
9 |
11/06 |
22. Node-red and Real time data monitoring 23. Issues in Deep learning 24. Quantum mechanics and Brownian Motion 25. Schreinger Equation |
Lecture Note |
[Homework #6 : Real-time data monitoring using Node-red] |
[Homeworks's due is at 18:00, on Nov 13th] |
|
| 10 |
11/13 |
26. Quatum Mechanics 27. Quantum Computing |
Lecture Note |
|||
|
11 |
11/20 |
28. Deep learning 29. Backpropagation 30. Issues in Deep Learning |
Lecture Note |
|||
|
12 |
11/24 |
31. Implementation using Deep Learning |
Lecture Note |
[Homework #7: Deep learning analysis] |
[Homeworks's due is at 18:00, on Nov 27th] |
|
| 13 |
11/27 |
32. Bias & Layers (And / XOR node) 33. Adaptive & Momentum method (SGDM, Momentum, Adagard, RMSProp, Adadelta, ADAM) 34. Type of energy function (MSE type, Entropy type) |
Lecture Note |
|||
|
14 |
12/04 |
35. Nonfunctional data analysis |
Lecture Note |
|||
|
15 |
12/18 |
Final exam |
Lecture Note |