|
週次 |
日期 |
單元主題 |
|
第1週 |
9/22 |
Week 1:
Lecture 01-1: Introduction to Optimization
Lecture 01-2: Introduction to Metaheuristics
Lecture 01-3: Traditional Methods: An Introduction |
|
第2週 |
9/29 |
Week 2:
Lecture 02-1: Single State Method #1: Simulated Annealing
Lecture 02-2: Single State Method #2: Tabu Search
Lecture 02-3: Comparison between Optimization Algorithms |
|
第3週 |
10/06 |
Week 3:
Lecture 03-1: Genetic Algorithm I: Fundamental Idea and Hypothesis
Lecture 03-2: Genetic Algorithm II: Basic Operators
Lecture 03-3: Genetic Algorithm III: Advanced Topics |
|
第4週 |
10/13 |
Week 4:
Lecture 04-1: Particle Swarm Optimization I: Basic Phenomena
Lecture 04-2: Particle Swarm Optimization II: Algorithm
Lecture 04-3: Particle Swarm Optimization III: Advanecd Topics |
|
第5週 |
10/20 |
Week 5:
Lecture 05-1: Ant Colony Optimization I: Basic Phenomena
Lecture 05-2: Ant Colony Optimization II: Algorithm
Lecture 05-3: Final Project |
|
第6週 |
10/27 |
Week 6: Dimension reduction of high-dimensional data
Lecture 1: Introduction
Lecture 2: Principal component analysis
Lecture 3: Applications to biomedical data
|
|
第7週 |
11/03 |
Week 7: Prediction—via Regularized regression
Lecture 1: Introduction
Lecture 2: Methods (regression with lasso, ridge, etc)
Lecture 3: Applications to precision medicine
|
|
第8週 |
11/10 |
mid-term exam |
|
第9週 |
11/17 |
Week 9: Prediction --- some Machine Learning methods
Lecture 1: Introduction
Lecture 2: Methods (k-Nearest neighbor, support vector machines)
Lecture 3: Applications to precision medicine
|
|
第10週 |
11/24 |
Week 10: Prediction --- some Machine Learning methods
Lecture 1: Introduction
Lecture 2: Methods (transfer learning, etc)
Lecture 3: Applications to Pesonalized medicine
|
|
第11週 |
12/01 |
Week 11: Matrix computation |
|
第12週 |
12/08 |
Week 12: Convex analysis, convex optimization and gradient methods |
|
第13週 |
12/15 |
Week 13: Alternating direction method of multipliers |
|
第14週 |
12/22 |
Week 14: Proximal gradient algorithms |
|
第15週 |
12/29 |
Week 15: Stochastic gradient descent algorithms and topics in deep learning |