機器學習概論與實作
Introduction to Machine Learning and Practice
| 節 | 週四 |
|---|---|
7 15:30–16:20 | 機器學習概論與實作 YL839 3 節連堂 |
8 16:30–17:20 | |
9 17:30–18:20 |
* 根據陽明交大上課時間表所列
1. state-of-the-art machine learning techniques 2. medical image/signal processing 3. deep neural network 4. learning by doing 本堂課將會以實體與同步遠距混成教學方式上課，非陽明校區同學可提早5分鐘進入虛擬教室準備上課，連結每周會公告於E3系統上
若有先修以下課程為佳: 1. Math: calculus, linear algebra, probabilities, and statistics 2. Programming skill: Python
1.學期作業、考試、評量 作業(50%) 期末專題(40%) 課堂表現(10%) Homework: pre-course request and programming assignments (50%) Term project (40%) Performance in class (10%) 2.教學方法及教學相關配合事項（如助教、網站或圖書及資料庫等） 1. To give you the concepts, the intuitions, and the tools you need to actually implement programs capable of learning from data. 2. Learning by practice 3. Reference links: Python self-learning (http://learnpython.org/), textbook example code (https://github.com/ageron/Handson-ML2)
- Deep learning
- Biomedical signal and image processing
- Machine learning
| 週次 | 主題 |
|---|---|
| 第 1 週 | 機器學習簡介Machine learning landscape (Ch1) |
| 第 2 週 | 如何進行機器學習 End-to-end machine learning project (Ch2) |
| 第 3 週 | 監督式學習與貝氏決策理論 Classification - Supervised learning and Bayesian decision theory (Ch3) |
| 第 4 週 | 降維度法 Dimensionality reduction (CH8) |
| 第 5 週 | 線性回歸模型 Training models: Linear Regression (Ch4) |
| 第 6 週 | 分類器與支援向量機器 Classification and support vector machines (Ch5) |
| 第 7 週 | 期末專題企劃與文獻報告(I)Term-project abstract presentation(I) |
| 第 8 週 | 期末專題企劃與文獻報告(II)Term-project abstract presentation(II) |
| 第 9 週 | 決策樹Decision tress (Ch6) |
| 第 10 週 | 整體學習與隨機森林 Ensemble Learning and Random Forests (Ch7) |
| 第 11 週 | 非監督式學習Unsupervised learning (Ch9) |
| 第 12 週 | 生物醫學影像與訊號處理Biomedical image and signal processing |
| 第 13 週 | 訓練深度神經網路 Training Deep Neural Networks (Ch11) |
| 第 14 週 | 深層電腦視覺卷積神經網路 Deep Computer Vision using Convolutional Neural Networks (Ch14) |
| 第 15 週 | 運用自動生成解碼器之表徵學習與生成學習Representation Learning and Generative Learning using Autoencoder (Ch17) |
| 第 16 週 | 期末專題報告 Term-project presentation |
Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd 作者： Geron Aurelien 原文出版社：O’Reilly Media 出版日期：2019/10/22 ISBN-13: 978-1492032649
- 地點
- 線上討論 或 陽明校區 圖資大樓 849
- 時間
- make by appointment
- 聯絡方式
- email: lfchen@nycu.edu.tw