| 週次 |
授課內容 |
| 第1週 |
Introduction to biological data. |
| 第2週 |
Data structure and pattern. |
| 第3週 |
Data sampling, mining and modeling. |
| 第4週 |
Data validation and virtualization. |
| 第5週 |
Data pattern recognition. |
| 第6週 |
Large-scale data (big-data) and image data processing. |
| 第7週 |
Introduction to R. |
| 第8週 |
R programing for large-scale data analysis. |
| 第9週 |
R programing for data visualization. |
| 第10週 |
Introduction to data dimension reduction. |
| 第11週 |
Introduction to data similarity. |
| 第12週 |
Introduction to data clustering. |
| 第13週 |
Introduction to Artificial Intelligence (AI) and machine learning |
| 第14週 |
Supervised machine learning. |
| 第15週 |
Unsupervised machine learning. |
| 第16週 |
Application of Neuro Network and Deep Learning in biological modeling. Discussion and Review.
Self-study - I: Practice on case study of causality analysis between factors.
Self-study - II: Practice on case study of explanatory machine learning. |
自主學習
內容 |
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