| 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 ) |
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2 |
09/10 |
2. Types of Mathematical Programming 3. Review for Linear Programming |
Lecture Note |
Homework #2 (Linear Programming) |
[Lecture03] [Lecture04] |
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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] |
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4 |
10/01 |
reading days |
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Due on Homework #3 &4(Midnight, Oct 4th ) |
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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 ) |
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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] |
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7 |
10/22 |
Reading day |
Lecture Note |
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| 8 |
10/29 |
14. Metaheuristics (I) : Simulated annealing 15. Data issue in Big data |
Lecture Note |
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9 |
11/05 |
Miderm Exam |
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10 |
11/12 |
16. Genetic algorithm 17. Small introduction to Harmony Search |
Lecture Note |
Homework #8 (Genetic algoirthm) |
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|
11 |
11/14 |
Miderm Exam Distribution and Explanation |
Lecture Note |
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Due on Homework #8(Midnight, Nov 15th ) |
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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 ) |
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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) |
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14 |
12/03 |
20. Deep learning Implimentation(I) |
Lecture Note |
Homework #12 (General deep learning) |
Due on Homework #12 (Midnight, Dec 6th ) |
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15 |
12/10 |
21. Deep learning Implementation (II) : General Deep Learning, Multi-input / Multi-output DNN, Convolutional neural network |
Lecture Note |
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| 16 |
12/16 |
22. Momentum methods 23. AdaGrad, RMS Prop, AdaDelta, Adam |
Lecture Note |
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17 |
12/17 |
Firnal Exam |
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