資料科學方法
Methods in data science
| 節 | 週一 |
|---|---|
3 10:10–11:00 | 資料科學方法 YS415 2 節連堂 |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
課程描述 Data science requires scientific methods, statistics, computerized processes, theoretical and computational algorithms, and systems to transform abstract knowledge and insights from data into interpretable results or prediction models. This course aims to develop mathematical, analytical and technical skills to generate a decision driven by data. 本課程的目標為 (一) Fundamentals of data science and statistics (二) Develop the concept of computational thinking (三) Programing ability in R language (四) Learn Computational Skills in data science (五) Aspects of machine learning strategies (六) Theory and application of artificial neural networks
Biostatistics, Calculus, Linear algebra
1. 以講義為主 2. 每個模型都需要學習數學與統計理論,計算,應用,以及程式語言分析 3. 電腦軟體資料分析使用R language
1. 期中作業 (35%)及一次期末報告(40%) 2. 出席率,隨堂考試,與作業檢討(25%)。
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Data Science and the R language |
| 第 2 週 | Skills in the R language |
| 第 3 週 | Fundamentals (Linear algebra and biostatistics) |
| 第 4 週 | Big Data Analytics in R |
| 第 5 週 | Principal component analysis (PCA) |
| 第 6 週 | Regularization Methods |
| 第 7 週 | Support Vector Machine |
| 第 8 週 | Midterm |
| 第 9 週 | Machine Learning Methods |
| 第 10 週 | Random Forrest |
| 第 11 週 | Gradient Boost Machine |
| 第 12 週 | Extreme Gradient Boost Machine |
| 第 13 週 | Theory and mathematical derivation in Artificial Neural Networks |
| 第 14 週 | Artificial Neural Networks |
| 第 15 週 | Convolutional Neural Networks |
| 第 16 週 | Final exam |
| 第 17 週 | |
| 第 18 週 |
None
- 聯絡方式
- 負責教師:郭炤裕(cyguo@nycu.edu.tw)