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

09/03

1. Course Organization / Introduction

 Lecture Note

Homework #1

(Course Homepage)

Due on Homework #1

(Midnight, September 12 )

[Lecture01]

 2

09/10

2. Types of Mathematical Programming

3. Review for Linear Programming

 Lecture Note

Homework #2

(Linear Programming)

 

[Lecture02]

[Lecture03]

[Lecture04]

3

09/24

4. Stochastic Programming : new vendor Problem

5. Nonlinear function & FONC

 Lecture Note

Homework #3 & #4

(Newsvendor problem & NLP function)

Due on Homework #2(Midnight, September 23rd )

[Lecture05]

 4

10/01

reading days

 

 

Due on Homework #3 &4(Midnight, Oct 4th )

 

 5

10/08

6. SONC

7. SDG method

8. Dual problem & NLP

9. Dulality Gap

Lecture Note

Homework #5 & #6

(NLP & Dual Problem)

Due on Homework #5 &6(Midnight, Oct 11th )

 

6

10/15

10. Multiobjective programming

11. Pareto Optimal

12. Reinforcement Learning

13. KKT condition

  Lecture Note

Homework #7

(Multi-stage reinforcement learning)

Due on Homework #7(Midnight, Oct 18th )

[Lecture06]

 7

10/22

Reading day

 Lecture Note

   

 

 8

10/29

14. Metaheuristics (I) : Simulated annealing

15. Data issue in Big data

 Lecture Note

     

9

11/05

Miderm Exam

 

   

[Sample exam]

10

11/12

16. Genetic algorithm

17. Small introduction to Harmony Search

 Lecture Note

Homework #8

(Genetic algoirthm)

 

[Lecture10]

 11

11/14

Miderm Exam Distribution and Explanation

Lecture Note

 

Due on Homework #8(Midnight, Nov 15th )

 

12

11/19

18. Deep learning (I)

: Neuron Design, Global optimization-based weight setting

Lecture Note

Homework #9

(Global optimum-based weight setting)

Due on Homework #9

(Midnight, Nov 22nd )

[Lecture12]

 13

11/26

19. Deep learning (II)

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

Lecture Note

Homework #10 & #11

(Data analysis and Backpropagation)

Due on Homework #10 & 11(Midnight, Nov 29th)

 

14

12/03

20. Deep learning Implimentation(I)

Lecture Note

Homework #12

(General deep learning)

Due on Homework #12

(Midnight, Dec 6th )

 

15

12/10

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

  Lecture Note

     

 16

12/16

22. Momentum methods

23. AdaGrad, RMS Prop, AdaDelta, Adam

Lecture Note

     

 17

12/17

Firnal  Exam