Week

Date

 Topic

 Reading

 Assignments

Notices and Dues

Notes

 1

09/02

1. Course Organization / Introduction

 Lecture Note

Homework #1

(Course Homepage)

Due on Homework #1

(Noon, September 11 )

[Lecture01]

 2

09/09

2. Graph and Network

3. Sufficien and Necessary Condition

4. Mixed Integer Linear Programming (MILP)

5. Shortest Path Programming

 Lecture Note

Homework #1-1

(Shortest Path Programming)

Due on Homework #1-1

(Noon, September 15 )

[Lecture02]

[Lecture03]

[Lecture04]

3

09/16

6.Types of Mathematical Programming (LP, NLP)

7. Review for Linear Programming : Rank

8. Easy Problem

9. Corrplot & P-Value

 Lecture Note

Homework #2

(Data Correlation)

Due on Homework #2

(Midnight, September 19)

 4

09/23

10. Stochastic Programming : new vendor Problem

11. Nonlinear function & FONC

12. SONC

13. SDG method

 Lecture Note

[Lecture05]

 5

09/30

8. Dual problem & NLP

9. Dulality Gap

Lecture Note

6

10/14

10. Multiobjective programming

11. Pareto Optimal

12. Reinforcement Learning

13. KKT condition

  Lecture Note

 7

10/21

Midterm exam

 8

10/28

14. Metaheuristics (I) : Simulated annealing

15. Data issue in Big data

 Lecture Note

9

11/04

16. Genetic algorithm

 Lecture Note

10

11/11

17. Small introduction to Harmony Search

 Lecture Note

 11

11/18

Miderm Exam Distribution and Explanation

Lecture Note

12

11/25

18. Deep learning (I)

: Neuron Design, Global optimization-based weight setting

Lecture Note

 13

12/02

19. Deep learning (II)

: tensor, Data analysis (P-value), Backpropagation, Tensor

Lecture Note

14

12/09

20. Deep learning Implimentation(I)

Lecture Note

15

12/16

21. Deep learning Implementation (II) : General Deep Learning, Multi-input / Multi-output DNN, Convolutional neural network

22. Momentum methods

23. AdaGrad, RMS Prop, AdaDelta, Adam

  Lecture Note

 16

12/16

Final Exam