| 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 |
09/02 |
1. Course Organization / Introduction |
Lecture Note |
Homework #1 (Course Homepage) |
Due on Homework #1 (Noon, September 11 ) |
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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 ) |
[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) |
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4 |
09/23 |
10. Inverse function of probability 12. Stochastic Programming : new vendor Problem |
Lecture Note |
Homework #3 (Stochastic forecasting) |
[Lecture05] |
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|
5 |
09/30 |
11. Nonlinear function 12. FONC & SONC 13. SDG method |
Lecture Note |
Homework #4 (Local Optimum for Nonlinear function) |
Due on Homework #4 (Noon, September 29) |
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|
6 |
10/14 |
14. Differential Equation and Differential programming 15. System Dyamics 16. Fundamentals of Elecrtronics 17. Simscape |
Lecture Note |
Due on Homework #4 (Midnight, October 13) |
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|
7 |
10/21 |
18. Dual problem & NLP 19. Dulality Gap 20. KKT Condition |
Lecture Note |
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| 8 |
10/28 |
Midterm exam |
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9 |
11/04 |
21. Metaheuristics (I) 22. Simulated annealing |
Lecture Note |
Homework #5 (Simulated Annealing) |
Due on Homework #5 (Midnight, November 7th) |
[Lecture09] |
|
10 |
11/11 |
23. Harmony Search 24. Genetic algorithm (I) |
Lecture Note |
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|
11 |
11/18 |
25. Data issue in Big data 26. Genetic algorithm (II) |
Lecture Note |
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12 |
11/25 |
27. Deep learning (I) : Neuron Design, Global optimization-based weight setting |
Lecture Note |
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|
13 |
12/02 |
28. Deep learning (II) : tensor, Data analysis (P-value), Backpropagation, Tensor |
Lecture Note |
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|
14 |
12/09 |
29. Deep learning Implimentation(I) |
Lecture Note |
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|
15 |
12/16 |
30. Deep learning Implementation (II) : General Deep Learning, Multi-input / Multi-output DNN, Convolutional neural network 31. Momentum methods 32. AdaGrad, RMS Prop, AdaDelta, Adam |
Lecture Note |
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| 16 |
12/16 |
Final Exam |
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