| 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. Types of Mathematical Programming 3. Review for Linear Programming |
Lecture Note |
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[Lecture03] [Lecture04] |
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3 |
09/16 |
4. Stochastic Programming : new vendor Problem 5. Nonlinear function & FONC |
Lecture Note |
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4 |
09/23 |
6. SONC 7. SDG method |
Lecture Note |
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5 |
09/30 |
8. Dual problem & NLP 9. Dulality Gap |
Lecture Note |
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6 |
10/14 |
10. Multiobjective programming 11. Pareto Optimal 12. Reinforcement Learning 13. KKT condition |
Lecture Note |
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7 |
10/21 |
Midterm exam |
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| 8 |
10/28 |
14. Metaheuristics (I) : Simulated annealing 15. Data issue in Big data |
Lecture Note |
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9 |
11/04 |
16. Genetic algorithm |
Lecture Note |
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|
10 |
11/11 |
17. Small introduction to Harmony Search |
Lecture Note |
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11 |
11/18 |
Miderm Exam Distribution and Explanation |
Lecture Note |
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12 |
11/25 |
18. Deep learning (I) : Neuron Design, Global optimization-based weight setting |
Lecture Note |
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13 |
12/02 |
19. Deep learning (II) : tensor, Data analysis (P-value), Backpropagation, Tensor |
Lecture Note |
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|
14 |
12/09 |
20. Deep learning Implimentation(I) |
Lecture Note |
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|
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 |
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| 16 |
12/16 |
Final Exam |
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