資料分析與機器學習
Data Analysis and Machine Learning
| 節 | 週一 |
|---|---|
A 18:30–19:20 | 資料分析與機器學習 KB202 3 節連堂 |
B 19:30–20:20 | |
C 20:30–21:20 |
* 根據陽明交大上課時間表所列
This course is designed to introduce students to the field of Machine Learning, starting from Python programming fundamentals to mastering core data science techniques. It covers essential topics including data cleaning, feature engineering, and mainstream machine learning algorithms (such as regression, classification, clustering, and dimensionality reduction). In addition to mastering practical tools like Scikit-learn, students will be introduced to Neural Networks and the PyTorch deep learning framework. By balancing theory with hands-on practice, students will develop the skills to process data, build models, and perform evaluations from scratch. The course concludes with a final project where students apply their knowledge to solve real-world data problems. 本課程旨在帶領學生進入機器學習的世界，從 Python 程式基礎出發，逐步深入資料科學的核心技術。課程涵蓋資料清理、特徵工程、以及主流的機器學習演算法（包含回歸、分類、分群與降維）。除了掌握 Scikit-learn 等實務工具，學生也將接觸類神經網路與 PyTorch 深度學習框架。透過理論與實作並重的教學方式，學生將具備從零開始處理數據、建構模型並進行評估的能力，最終能獨立完成具備實務意義的期末專案，解決現實世界中的數據問題。
(建議) Python 程式語言
課堂實作平均：60% 期中考：20% 期末專案：20%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Machine Learning (機器學習簡介) |
| 第 2 週 | Python Basics (Python 基礎) |
| 第 3 週 | Scientific Computing and Data Analysis in Python: NumPy and Pandas (Python 科學計算與資料分析：NumPy and Pandas) |
| 第 4 週 | 教師節放假 |
| 第 5 週 | Data Visualization: Matplotlib and Seaborn (Python 資料視覺化：Matplotlib, Seaborn) |
| 第 6 週 | Machine Learning in Python: Scikit-learn (Python 機器學習：Scikit-learn) |
| 第 7 週 | Data Cleaning and Feature Engineering . Model Selection and Evaluation (資料清理與特徵工程. 模型選擇與評估) |
| 第 8 週 | 光復節補假 |
| 第 9 週 | Midterm Exam (期中考) |
| 第 10 週 | Regression and Applications (回歸與應用) |
| 第 11 週 | Classification and Applications (分類與應用) |
| 第 12 週 | Dimensionality Reduction and Applications (降維與應用) |
| 第 13 週 | Clustering and Applications (分群與應用) |
| 第 14 週 | Neural Networks (Deep Learning) (類神經網路 / 深度學習) |
| 第 15 週 | Deep Learning in Python: PyTorch (Python 深度學習：PyTorch) |
| 第 16 週 | Final Project (期末專案) |
J. Vanderplas, Python Data Science Handbook: Essential Tools for Working with Data, 2nd Edition, O'Reilly Media, 2023. (ISBN: 9781098121228)