| 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.
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Week |
Date |
Topic |
Reading |
Assignments |
Notices and Dues |
Notes |
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1 |
09/06 |
1. Introduction 2. System and Network 3. Data issues |
Lecture Note |
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1. Prediction in Network 2. Quantum Mechanics |
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2 |
09/13 |
4. Stabale network Vs Unstable network 5. Differential Equation-based representation 6. Brownian motion (I) |
Lecture Note |
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3 |
09/20 |
Reading day (Korean Thanksgiving holiday) |
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4 |
09/27 |
7. Brownian motion (II) 8. Kolmogorove's Backward Equation 9. Geometric Brownian Motion 10. Riemmann-Stiletjes Integral 11. L2 Convergence 12. Ito-Integral |
Lecture Note |
Literature Review for Quantum-based data anaysis |
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5 |
10/04 |
13. Winer Process 14. Differential equation for Winder Process |
Lecture Note |
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6 |
10/11 |
Reading days |
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7 |
10/18 |
15. Presentation for Quantum mechanism-based research studies |
Lecture Note |
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Due for Homework #1 |
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8 |
10/18 |
16.Geometric Brownian Motion 17. Orinsten-Uhlenbect Process 18. Ito Lemma |
Lecture Note |
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9 |
11/01 |
Midterm Exam |
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10 |
11/08 |
19. Ito Lemma, Cont'd 20. Geometric Brownian Motion (II) |
Lecture Note |
1. Population growth model | ||
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11 |
11/15 |
21. Deep Learning Implementation (I) |
Lecture Note |
G701 (Lecture room) |
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12 |
11/22 |
22. Deep Learning Implementation (II) : Rucurrent Neural Network 23. RUL Estimation |
Lecture Note |
[Link] |
G701 (Lecture room) |
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13 |
11/29 |
25. Python in Matlab 26. Activation functions and Data normalization 27. Classification Vs Prediction |
Lecture Note |
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14 |
12/06 |
28. Variational Nonlinear Optimization for Deep Learning |
Lecture Note |
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15 |
12/13 |
Final Exam |
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