數據科學矩陣方法(英文授課)
Matrix Methods in Data Science
學期
108-1
學分
3
學分
當期課號
5948
永久課號
IDS5009
開課單位
數據科學與工程研究所碩士班
授課教師
彭文孝
校區
光復
類別
選修
上課時間表
| 節 | 週二 | 週四 |
|---|---|---|
3 10:10–11:00 | 數據科學矩陣方法(英文授課) EC122 2 節連堂 | |
4 11:10–12:00 | ||
8 16:30–17:20 | 數據科學矩陣方法(英文授課) EC122 |
* 根據陽明交大上課時間表所列
概述
This course extends matrix methods in Linear Algebra to cover their applications to data science, including data analysis, signal processing, and machine learning. It shall equip students with the required matrix methods on which data science depends.
先修科目
1. Linear Algebra
評分方式
Mid-term x 2 -- 50% Final x 1 -- 30 % Homework (including computer assignments) – 20% Project (??)
課程大綱
- Highlights of Linear Algebra
- Computations with Large Matrices
- Low Rank and Compressed Sensing
- Special Matrices
- Optimization
- Learning form data
週次計畫
| 週次 | 主題 |
|---|---|
| 第 1 週 | Highlights of Linear Algebra |
| 第 2 週 | Highlights of Linear Algebra |
| 第 3 週 | Highlights of Linear Algebra |
| 第 4 週 | Computations with Large Matrices |
| 第 5 週 | Computations with Large Matrices |
| 第 6 週 | Computations with Large Matrices |
| 第 7 週 | Low Rank and Compressed Sensing |
| 第 8 週 | Low Rank and Compressed Sensing |
| 第 9 週 | Low Rank and Compressed Sensing |
| 第 10 週 | Special Matrices |
| 第 11 週 | Special Matrices |
| 第 12 週 | Special Matrices |
| 第 13 週 | Optimization |
| 第 14 週 | Optimization |
| 第 15 週 | Optimization |
| 第 16 週 | Learning form data |
| 第 17 週 | Learning form data |
| 第 18 週 | Learning form data |
教科書
Text: “Linear Algebra and Learning from Data,” Gilbert Strang, 1st Ed., published by Wellesley-Cambridge Press, 2019. Online resources: https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/