MATLAB進階程式設計與專題實作
MATLAB Advanced Programming Design
| 節 | 週四 |
|---|---|
7 15:30–16:20 | MATLAB進階程式設計與專題實作 YT203 2 節連堂 |
8 16:30–17:20 |
* 根據陽明交大上課時間表所列
This course will introduce the concepts and applications of machine learning using MATLAB programming language. Students will learn the theoretical basis of machine learning from concept introduction, regression model, data classification, convolutional neural network to model selection and validation. We have MATLAB practice examples for demonstration. Students in this course will also use the content of this course to conduct project work, either through online data or their own research topics, in order to enhance their interest in computer science and machine learning, and to help them develop applications in their professional subjects.
It is required to have basic knowledge of MATLAB programming language.
Introduction to the course (1 week), introduction to machine learning concepts (2 weeks), regression models (2 weeks), data clustering (1 week), data classification (3 weeks), convolutional neural networks (2 weeks), model selection and validation (2 weeks), and final team project (3 weeks).
1. Attendance and participation: 30% 2. Midterm written proposal (1 to 2 pages): 30% 3. Final project report (oral presentation): 40%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Course introduction |
| 第 2 週 | An overview of machine learning |
| 第 3 週 | Representation of data for machine learning |
| 第 4 週 | Linear and nonlinear regression |
| 第 5 週 | Unsupervised learning: Clustering |
| 第 6 週 | off (兒童節及民族掃墓節連假) |
| 第 7 週 | Classification: tree-based methods |
| 第 8 週 | Classification: support vector machines |
| 第 9 週 | MATLAB Graphic User Interface: App designer I |
| 第 10 週 | MATLAB Graphic User Interface: App designer II |
| 第 11 週 | Classification: neural networks |
| 第 12 週 | Deep Learning & Convolutional neural networks |
| 第 13 週 | MATLAB Deep Network Designer |
| 第 14 週 | Deep Learning Applications (Object detection, Tumor segmentation, Image transformation) |
| 第 15 週 | Resampling methods and model validation |
| 第 16 週 | Final project report |
[Textbook 1] A First Course in Machine Learning, 2nd edition, 2017 Simon Rogers, Mark Girolami CRC Press https://github.com/sdrogers/fcmlcode [Textbook 2] MATLAB Machine Learning Recipes, 2nd edition, 2018 Michael Paluszek, Stephanie Thomas Apress https://github.com/Apress/matlab-machine-learning-recipes [Textbook 3] An Introduction to Statistical Learning, 2nd edition, 2013 Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Springer [Reference] Matlab Deep Learning Toolbox User's Guide, 2020 Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth Mathworks