機器學習與商業應用
Machine Learning and Business Applications
| 節 | 週三 |
|---|---|
5 13:20–14:10 | 機器學習與商業應用 TD 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
Artificial Intelligence (AI) is of fundamental importance in commerce technology and Machine Learning (ML) offers most of the tools in AI. We will introduce various techniques in ML in the course that can be applied to various fields in business such as marketing and finance. Through actual implementation of the learned ML techniques the students will also develop the trends in commerce technology.
Statistics, R programming language (preferred but NOT required)
1. Five or six Homework and Assignments will be assigned which require the use of R programming language. 2. Midterm and Final project presentations will be required. A written project report will be due at the end of the semester. 3. Evaluation and Grading Policy: Homework 30% + Midterm/Final oral presentation 20% each + Final written report 20% + Other, 10%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction |
| 第 2 週 | R programming language |
| 第 3 週 | Supervised and Unsupervised Learning + R practice |
| 第 4 週 | LDA and QDA + R practice |
| 第 5 週 | Linear Regression: OLS, Logistic Regression + R practice |
| 第 6 週 | K-nearest neighboring + R practice |
| 第 7 週 | Decision Tree + R practice |
| 第 8 週 | Random Forest + R practice |
| 第 9 週 | Midterm Presentations |
| 第 10 週 | Simple Bayes classification methods networks + R practice |
| 第 11 週 | Support Vector Machine + R practice |
| 第 12 週 | Ensemble Learning: Boosting, Bagging + R practice |
| 第 13 週 | Neuro network and applications + R practice |
| 第 14 週 | Neuro network + R practice |
| 第 15 週 | Deep Learning and Reinforcement Learning + R practice |
| 第 16 週 | Association rule learning + R practice |
| 第 17 週 | Nonsupervised Learning and Clusttering Analysis + R practice |
| 第 18 週 | Final Presentations |
1. “An Introduction to Statistical Learning with Applications in R” by James, Witten, Hastie, and Tibshirani (2017), free download from http://wwwbcf.usc.edu/~gareth/ISL/index.html 2. “An Introduction to R” by Venables and Smith (2018), free download from http://cran.r-project.org/doc/manuals/R-intro.pdf. 3. 簡禎富、許嘉裕 (2014),資料挖礦與大數據分析,前程文化。 4. 何宗武 (2016),R資料採礦與數據分析,碁峯。 5. 酆士昌 (2016),R語言:數學計算、統計模型與金融大數據分析,博碩。