| 課程編碼 Course Code | 中文課程名稱 Course Name (Chinese) | 英文課程名稱 Course Name (English) | 總學分數 Credits | 總時數 Hours |
|---|---|---|---|---|
| 3104130 | 機器學習基礎 | Fundamentals of Machine Learning | 3.0 | 3 |
| 中文概述 Chinese Description | 機器學習軟體可用於設計和訓練豐富且靈活的機器學習系統，具備數學基礎以理解機器學習的基本原理將是重要的，掌握這些原理可了解目前使用的方法的固有假設和局限性，有助於促進創建新的機器學習解決方案。課程將分兩部分來進行，第一部分主要闡述相關的數學基礎包含線性代數、解析幾何、矩陣分解、機率與最佳化等學理，第二部分則將第一部分中的概念應用於基本機器學習包含回歸、維度減少、密度估計和分類等應用。 | |||
| 英文概述 English Description | While machine learning software is available to design and train rich and flexible machine learning systems, the mathematical foundations of machine learning are important in order to understand fundamental principles for creating new machine learning solutions, and learn about the inherent assumptions and limitations of the methodologies. This course will contain two parts, where Part I lays the mathematical foundations in linear algebra、analytic geometry、matrix decomposition、probability theory and optimization , and Part II applies the concepts from Part I to a set of fundamental machine learning problems including regression, dimensionality reduction, density estimation, and classification. | |||
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