本課程將介紹機器學習的基本原理，認識不同機器學習方法及背後操作方式，並透過簡單的物理範例，學生將認識如何編寫機器學習程式以解決真實應用問題。在課程中，學生將認識機器學習中主要的演算法，包括監督學習、非監督學習和強化學習，學習不同機器學習演算法如何實質解決許多傳統演算法無法有效處理的問題。認識機器學習的標準作業流程，當中將包括資料收集、資料預處理、神經網路模型選取和建立、模型訓練和預測結果等。學生將使用Python程式語言，學習並利用Tensorflow和Keras套件在機器學習演算法的應用，嘗試實際操作在物理相關的應用題目上。This course will introduce the basic principles of machine learning, understand different machine learning methods and the operations behind them, and through simple physics examples, students will understand how to write machine learning programs to solve real application problems. In the course, students will learn about the main algorithms in machine learning, including supervised learning, unsupervised learning and reinforcement learning, and learn how different machine learning algorithms can actually solve many problems that traditional algorithms cannot effectively handle. Understand the standard operating procedures of machine learning, which will include data collection, data preprocessing, neural network model selection and establishment, model training and prediction results, etc. Students will use the Python programming language, learn and utilize the Tensorflow and Keras suites in the application of machine learning algorithms, and try to practice practical application problems related to physics.
Textbook: Python Machine Learning 2nd, Sebastian Raschka & Vahid Mirjalili
Online course: Standford university, Andrew Ng, https://zh-tw.coursera.org/learn/machine-learning
Resources: https://blog.kdchang.cc/2016/10/09/how-to-mastering-machine-learning-with-python/
Textbook: Python Machine Learning 2nd, Sebastian Raschka & Vahid Mirjalili
Online course: Standford university, Andrew Ng, https://zh-tw.coursera.org/learn/machine-learning
Resources: https://blog.kdchang.cc/2016/10/09/how-to-mastering-machine-learning-with-python/
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 課堂實作課堂實作 Classroom practice |
20 | |
| 期中考期中考 midterm exam |
40 | |
| 期末報告期末報告 Final report |
40 |
