數位內容與機器學習
Digital Content and Machine Learning
| 節 | 週一 |
|---|---|
3 10:10–11:00 | 數位內容與機器學習 MB312（光復） 3 節連堂 |
4 11:10–12:00 | |
N 12:20–13:10 |
* 根據陽明交大上課時間表所列
The course is to introduce the theoretical background of the digital content and the technique of content processing and applications. The class participants should be able to learn a broad and deep account of technology with an inside understanding of digital content, system and its practical implementation. Due to the recent progress of artificial intelligence and machine learning, the tools in MATLAB will be applied to learn the deep learning models, parameters optimization and not over-fitting for prediction accuracy. The applications will be introduced for time series analysis, pattern recognition in electronic commerce.
Basic Computer Programming
無備註
Features of Teaching (design of materials, pedagogy, evaluation, resources and other facilities): Lectures are given in slides and HTML format. Grading: mid-term 20%; final exam 40%; class participation and home works 40%
Features of Teaching (design of materials, pedagogy, evaluation, resources and other facilities): Lectures are given in slides and HTML format. Grading: mid-term 20%; final exam 40%; class participation and home works 40%
Special Topic Discussion
1. content retrieval 2. interactivity 3. Latest progress
- 講授：
- 8
- 示範：
- 2
- 實作：
- 6
- 其他：
- 2
Course fundamental
1. digital content introduction 2. multimedia data format 3. Image and Video 4. Audio
- 講授：
- 12
- 示範：
- 6
- 實作：
- 12
- 其他：
- 6
| 週次 | 主題 |
|---|---|
| 第 1 週 | Overview and Introduction 線上上課鏈結:https://meet.google.com/dre-bvvh-fsz |
| 第 2 週 | Fundamentals |
| 第 3 週 | Data presentation and transformation |
| 第 4 週 | Video and audio integration |
| 第 5 週 | Video editing techniques |
| 第 6 週 | MATLAB tools |
| 第 7 週 | MPEG1,MPEG2,MPEG4, H.264,MP3 |
| 第 8 週 | Digital Right Management |
| 第 9 週 | Midterm report |
| 第 10 週 | Digital content implementation in electronic commerce |
| 第 11 週 | Machine learning introduction |
| 第 12 週 | Supervised Learning and non-supervised Learning |
| 第 13 週 | Neural network and deep learning architecture |
| 第 14 週 | The method of Optimization |
| 第 15 週 | Support Vector Machine Introduction |
| 第 16 週 | Convolutional Neural Network Introduction |
| 第 17 週 | Future techniques discussion |
| 第 18 週 | Final exam |
Reference Book(s): 1. Digital Multimedia, Nigel Chapman and Jenny Chapman, John Wiley 2. Machine Learning in Python : Essential Techniques for Predictive Analysis, Michael Bowles, Wiley. 3. Handouts and selected journal papers
- 地點
- MB304 線上上課鏈結:https://meet.google.com/dre-bvvh-fsz
- 時間
- Tuesday CD
- 聯絡方式
- Ext. 57406 e-mail: mjtsai@cc.nctu.edu.tw