Topics
- Statistical Learning (linear regression and classification, sampling, model selection)
- Artificial Neural Networks
- Design of Experiments
- Response Surface Methodology
- Metaheuristic Optimization
- Multi-Objective Optimization
Schedule
Week 1 (Statistical Learning)
- Wed Jan 22
-
- LEC 1Intro & Logistics
- Setup env; Git & GitHub (opt)
-
LEC 2Statistical Learning
Week 2
- Mon Jan 27
- LEC 3Statistics Review
- Wed Jan 29
- LEC 4Linear Regression
Week 3
- Mon Feb 3
-
- LEC 4Linear Regression
- HW 1: Linear Regression
- HW 1 Assigned
- Wed Feb 5
- LEC 5Classification
Week 4
- Mon Feb 10
- LEC 5Classification
- Wed Feb 12
- LEC 6Resampling
Week 5
- Mon Feb 17
- LEC 7Selection
- HW 1 Due
- Wed Feb 19
-
- LEC 8Trees
- HW 2: Classification
- HW 2 Assigned
Week 6
- Mon Feb 24
- LEC 8Trees
- Wed Feb 26
- NO CLASS
- Fri Feb 28
- HW 2 Due
Week 7
- Mon Mar 3
- NO CLASS
- Wed Mar 5
- LEC 9Neural Networks
Week 8
- Mon Mar 10
- EXAMExam 1
- Wed Mar 12
- NO CLASS
Week 9 (Spring Break)
- Mon Mar 17
- NO CLASS
- Wed Mar 19
- NO CLASS
Week 10 (Design Optimization)
- Mon Mar 24
-
- LEC 9Neural Networks
- Project 1: Statistical Learning
- Proj 1 Assigned
- Wed Mar 26
- LEC 9Neural Networks
Week 11
- Mon Mar 31
- LEC 10Design of Experiments
- Wed Apr 2
-
- LEC 10Design of Experiments
- HW 4: Design of Experiments
- HW 4 Assigned
Week 12
- Mon Apr 7
- LEC 11Optimization
- Proj 1 Due
- Wed Apr 9
-
- LEC 11Optimization
- Project 2: Design Optimization
- Proj 2 AssignedHW 4 Due