查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

111-1 308806 機器學習基礎 1 3.0 3 ★ 劉俊麟電機四甲
電機四乙
電機四丙 20 2 ◎電機四甲乙丙合開

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
111-1	308806	機器學習基礎	1	3.0	3	★	劉俊麟	電機四甲電機四乙電機四丙	20	2	◎電機四甲乙丙合開

教學大綱與進度：

教師姓名劉俊麟　教師諮商時間(Office Hours)

Email junlin@mail.ntut.edu.tw

最後更新時間 2022-08-04 09:35:37

課程大綱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。 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.

課程進度 Week1 Introduction Week2 Linear Algebra Week3 Linear Algebra Week4 Analytic Geometry Week5 Analytic Geometry Week6 Matrix Decomposition Week7 Matrix Decomposition Week8 Midterm Exam. Week9 Vector Calculus Week10 Vector Calculus Week11 Probability & Distributions Week12 Probability & Distributions Week13 Continuous Optimization Week14 When Model Meet Data Week15 Linear Regression Week16 Dimension Reduction with PCA Week17 Density Estimation Week18 Final Exam.

評量方式與標準出席: 10% (點名5次, 1次未到扣2%)，參與度: 5% 作業: 25%，期中考: 30%，期末考：30%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
Textbook: Mathematics for Machine Learning by Marc Peter Deisenroth, et al., Cambridge University Press

課程諮詢管道 email: junlin@ntut.edu.tw 聯繫以進行email 方式課程諮詢或安排線上會議諮詢，或於教師面對面諮商時段(待公佈)進行課程諮商討論

備註
疫情嚴重期間將依學校公告改採線上教學，線上會議軟體將採用如MS Teams或Google Meet等

教師姓名	劉俊麟　教師諮商時間(Office Hours)
Email	junlin@mail.ntut.edu.tw
最後更新時間	2022-08-04 09:35:37
課程大綱	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。 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.
課程進度	Week1 Introduction Week2 Linear Algebra Week3 Linear Algebra Week4 Analytic Geometry Week5 Analytic Geometry Week6 Matrix Decomposition Week7 Matrix Decomposition Week8 Midterm Exam. Week9 Vector Calculus Week10 Vector Calculus Week11 Probability & Distributions Week12 Probability & Distributions Week13 Continuous Optimization Week14 When Model Meet Data Week15 Linear Regression Week16 Dimension Reduction with PCA Week17 Density Estimation Week18 Final Exam.
評量方式與標準	出席: 10% (點名5次, 1次未到扣2%)，參與度: 5% 作業: 25%，期中考: 30%，期末考：30%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 Textbook: Mathematics for Machine Learning by Marc Peter Deisenroth, et al., Cambridge University Press
課程諮詢管道	email: junlin@ntut.edu.tw 聯繫以進行email 方式課程諮詢或安排線上會議諮詢，或於教師面對面諮商時段(待公佈)進行課程諮商討論
備註	疫情嚴重期間將依學校公告改採線上教學，線上會議軟體將採用如MS Teams或Google Meet等