Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2023 323314 Fundamentals of Machine Learning 1 3.0 3 ★ Jun-Lin Liu 4EE4A
4EE4B
4EE4C 11 0 電機四甲乙丙合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2023	323314	Fundamentals of Machine Learning	1	3.0	3	★	Jun-Lin Liu	4EE4A 4EE4B 4EE4C	11	0	電機四甲乙丙合開

Course Plan：

Instructor Jun-Lin Liu　Office Hours

Email junlin@mail.ntut.edu.tw

Last Updated 2023-08-14 09:50:31

Course Objective 理解機器學習(Machine Learning)的理論基礎與原理是重要的，若能掌握機器學習相關之數學基礎，將對機器學習所採行的方法，能進一步了解其原理與局限性等，有助於未來面對新問題時，能有效應用與建構出以機器學習為架構的解決方案。本課程將以兩部份來進行，第一部份以介紹機器學習相關的理論基礎為主包含線性代數(Linear Algebra)、幾何解析 (Analytic Geometry)、矩陣分解(Matrix Decomposition)、機率(Probability)與最佳化(Optimization)等；第二部份則輔以機器學習中具經典代表性之應用案例如線性回歸(Linear Regression)、維度降低(Dimension Reduction)、密度估計(Density Estimation)和分類(Classification)等進行深入的探討與分析。

Course Schedule *** Linear Algebra *** Week1-2: Course Overview, Linear Algebra, Linear Equation, Matrics, Vector Space (Chapter 2) Week3-4: Analytic Geometry, Inner Product, length & Angle, Orthonormal Basis, Orthogonal Projection (Chapter 3) Week5-6: Matrix Decomposition, Determine, Eigenvalues, Eigenvectors, Eigendecomposition and Diagonalization (Chapter 4) Week7: When Models Meet Data (Part II, Chapter 8) Week8: Dimension Reduction with Principal Component Analysis(PCA) (Part II, Chapter 10) Week9: Midterm Exam. *** Probability *** Week10-11: Vector Calculus, Partial Differentiation and Gradients (Chapter 5) Week12-13: Probability & Distributions, Probability Space, Discrete and Continuous Probability (Chapter 6) Week14-15: Linear Regression, Parameter Estimation, Bayesian Linear Regression (Part II, Chapter 9) *** Optimization *** Week16-17: Continuous Optimization, Gradient Descent, Convex Optimization (Chapter 7) Week18 Final Exam.

Evaluation and grading policy 出席: 10% 作業: 30% 期中考: 30% 期末考：30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Textbook: Mathematics for Machine Learning by Marc Peter Deisenroth, et al., 2020, Cambridge University Press (東華書局代理) Reference: 1. 機器學習原理深究, 李航，深智 2. 30分鐘就讀懂：機器學習從數學開始(第二版)，張曉明，深智 3. https://zhuanlan.zhihu.com/p/35449496

The access to curricular consultation 1. Teacher office hours for 112-1 @綜科館215:
Tue. 10:00 ~ 12:00, Wed. 15:00 ~ 17:00
2. Please email junlin@mail.ntut.edu.tw to reserve the time for face-to-face or online curricular consultation in advance.

The course corresponds to the SDGs

Note
因應疫情發展，本學期教學及授課方式請依照學校網頁所公布之訊息為準：
(https://oaa.ntut.edu.tw/p/404-1008-98622.php?Lang=zh-tw)

Instructor	Jun-Lin Liu　Office Hours
Email	junlin@mail.ntut.edu.tw
Last Updated	2023-08-14 09:50:31
Course Objective	理解機器學習(Machine Learning)的理論基礎與原理是重要的，若能掌握機器學習相關之數學基礎，將對機器學習所採行的方法，能進一步了解其原理與局限性等，有助於未來面對新問題時，能有效應用與建構出以機器學習為架構的解決方案。本課程將以兩部份來進行，第一部份以介紹機器學習相關的理論基礎為主包含線性代數(Linear Algebra)、幾何解析 (Analytic Geometry)、矩陣分解(Matrix Decomposition)、機率(Probability)與最佳化(Optimization)等；第二部份則輔以機器學習中具經典代表性之應用案例如線性回歸(Linear Regression)、維度降低(Dimension Reduction)、密度估計(Density Estimation)和分類(Classification)等進行深入的探討與分析。
Course Schedule	* Linear Algebra * Week1-2: Course Overview, Linear Algebra, Linear Equation, Matrics, Vector Space (Chapter 2) Week3-4: Analytic Geometry, Inner Product, length & Angle, Orthonormal Basis, Orthogonal Projection (Chapter 3) Week5-6: Matrix Decomposition, Determine, Eigenvalues, Eigenvectors, Eigendecomposition and Diagonalization (Chapter 4) Week7: When Models Meet Data (Part II, Chapter 8) Week8: Dimension Reduction with Principal Component Analysis(PCA) (Part II, Chapter 10) Week9: Midterm Exam. * Probability * Week10-11: Vector Calculus, Partial Differentiation and Gradients (Chapter 5) Week12-13: Probability & Distributions, Probability Space, Discrete and Continuous Probability (Chapter 6) Week14-15: Linear Regression, Parameter Estimation, Bayesian Linear Regression (Part II, Chapter 9) * Optimization * Week16-17: Continuous Optimization, Gradient Descent, Convex Optimization (Chapter 7) Week18 Final Exam.
Evaluation and grading policy	出席: 10% 作業: 30% 期中考: 30% 期末考：30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Textbook: Mathematics for Machine Learning by Marc Peter Deisenroth, et al., 2020, Cambridge University Press (東華書局代理) Reference: 1. 機器學習原理深究, 李航，深智 2. 30分鐘就讀懂：機器學習從數學開始(第二版)，張曉明，深智 3. https://zhuanlan.zhihu.com/p/35449496
The access to curricular consultation	1. Teacher office hours for 112-1 @綜科館215: Tue. 10:00 ~ 12:00, Wed. 15:00 ~ 17:00 2. Please email junlin@mail.ntut.edu.tw to reserve the time for face-to-face or online curricular consultation in advance.
The course corresponds to the SDGs
Note	因應疫情發展，本學期教學及授課方式請依照學校網頁所公布之訊息為準： (https://oaa.ntut.edu.tw/p/404-1008-98622.php?Lang=zh-tw)