Week

Date

 Topic

 Reading

 Assignments

Notices and Dues

Notes

 1

03/05

1. Introduction

2. Probability, Statistics and Network

3. Data, Network and measure

 Lecture Note

Installation of Matlab

(Full Toolboxes)

1. Bayesian network

2. Forecasting and deep learning

 2

03/12

4. Measure and data

5. Network Complexity

6. Big data

 Lecture Note

1. Network conversion

2. Network complexity (node, edge)

3. Rank

3

03/19

7. FONC and KKT condition

8. Dual problem

9. deep neural network and deep learning and Network

10. sigmoid function

Lecture Note

Data for deep learning analysis

Due on Installation of Matlab

(Full Toolboxes)

4

03/26

11. back propagation

12. system, process, Hebbian learning

Lecture Note

Due on Data for deep learning analysis

[Link]

5

04/02

13. Matlab Introduction

14. Deep learning using Matlab

Lecture Note

Deep learning analysis

[Link]

6

04/09

15. Bias in Deep Learning

16. Other activation functions

17. Deep learning using Matlab (II)

Lecture Note

[Link]

7

04/16

18. Momentum, Adagrad, RMS Prop,  Adadelta and Adam

19. stable data and unstable data

20. Trainsient analysis and inhomogenous system

Lecture Note

Due on Deep learning analysis

8

04/23

21. Review of Midterm

Lecture Note

9

04/30

Midterm

10

05/07

22. Engineering Transformation

Lecture Note

Laplace Transformation

Fourier Transformation

11

05/14

23.Digital Twin & Cyber Physical System

24. Data Transformation - Invariant Transformation (Rotation-based transformation)

Lecture Note

12

05/21

Lecture Note

[Link]

13

05/28

26. Deep Reinforcement Learning

Lecture Note

14

06/07

27.Deep Learning Implementation without any library and toolboxes

Lecture Note

15

06/11

28.Deep Reinforement Learning, revisited

29. P-value and R-square

16

 06/18

Final Exam