| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
|
|
|
|
| Quiz |
| Attendance |
| Assignment |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Course Info |
| Week 2 |
Mathematical Review |
| Week 3 |
Mathematical Review
Optimization Fundamentals
|
| Week 4 |
Unconstrained Optimization |
| Week 5 |
Unconstrained Optimization |
| Week 6 |
Unconstrained Optimization |
| Week 7 |
Application: Least-Square Analysis |
| Week 8 |
Application: Least-Square Analysis
Application: Neural Networks for Machine Learning |
| Week 9 |
Midterm exam |
| Week 10 |
Application: Practical Guide to Neural Network Design |
| Week 11 |
Application: Practical Guide to Neural Network Design |
| Week 12 |
Constrained Optimization |
| Week 13 |
Constrained Optimization |
| Week 14 |
Constrained Optimization |
| Week 15 |
Search for Global Optimum |
| Week 16 |
Search for Global Optimum |
| Week 17 |
Multiobjective Optimization |
| Week 18 |
Final exam |
|
| Evaluation |
Homework
Midterm exam
Final exam |
| Textbook & other References |
References:
1. E. K. P. Chong and S. H. Zak. An Introduction to Optimization (4th edition), John Wiley & Sons, 2013.
2. A. Ravindran et al. Engineering Optimization: Methods and Applications (2nd edition), John Wiley & Sons, 2006.
3. S. S. Rao. Engineering Optimization: Theory and Practice (4th edition), John Wiley & Sons, 2009.
4. MATLAB Optimization Toolbox User’s Guide, MathWorks Inc.
5. MATLAB Global Optimization Toolbox User’s Guide, MathWorks Inc. |
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