數值分析
Numerical Method
學期
114-2
學分
3
學分
當期課號
132310
永久課號
BEBE20014
開課單位
生物醫學工程學系
授課教師
鄔蜀威
校區
陽明
類別
選修
上課時間表
| 節 | 週三 |
|---|---|
2 09:00–09:50 | 數值分析 YEA200 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
概述
Numerical analysis is the study of numerical algorithms that attempt to find approximate solutions of problems rather than the exact ones. This course investigates computational methods in data science, which can be leveraged to improve artificial intelligent algorithms, such as equation solvers, curve fitting, optimization, machine learning, and deep learning.
評分方式
Attendance Rate 30% Midterm Exam 30% Final Exam 40%
週次計畫
| 週次 | 主題 |
|---|---|
| 第 1 週 | 1. Introduction 2. Errors 3. The Taylor series |
| 第 2 週 | 1. Interpolation and curve fitting |
| 第 3 週 | 1. Solutions of equations of one variable and simultaneous linear algebraic equations |
| 第 4 週 | 1. Numerical integration 2. Numerical differentiation |
| 第 5 週 | 1. Midterm exam |
| 第 6 週 | 1. Introduction to machine learning |
| 第 7 週 | 1. Optimization 2. Calculus of variations |
| 第 8 週 | 1. Algorithms of unsupervised learning |
| 第 9 週 | 1. Regression analysis |
| 第 10 週 | 1. Support vector machine |
| 第 11 週 | 1. Probabilistic models |
| 第 12 週 | 1. Decision tree 2. k-nearest neighbors algorithm |
| 第 13 週 | 1. Algorithms of unsupervised learning |
| 第 14 週 | 1. Principal component analysis |
| 第 15 週 | 1. Reinforcement learning 2. Monitoring and evaluation |
| 第 16 週 | Final Exam |
教科書
Textbook not required