Course Syllabus

Course Information：

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

Spring Semester
Year 2024 329021 Artificial intelligent and machine learning 1 3.0 3 ★ Shih-Wen Chen GME
GAT
DME
機電外國學生專班 53 3 機電所、自動化所、機電博和機電博外生班合開合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2024	329021	Artificial intelligent and machine learning	1	3.0	3	★	Shih-Wen Chen	GME GAT DME 機電外國學生專班	53	3	機電所、自動化所、機電博和機電博外生班合開合開

Course Plan：

Instructor Shih-Wen Chen　Office Hours

Email shihwen@mail.ntut.edu.tw

Last Updated 2024-05-20 21:10:23

Course Objective Introduction to machine learning by Python programming. Regression analysis, dimension reduction, feature extraction, classification methods, support vector machines, and neural networks. Python examples to improve students' knowledge of machine learning.

Course Schedule week1: Introduction to machine learning week2: Introduction to python week3: Probability and optimization week4: Feature Extraction week5: Bayesian decision theory week6: Linear regression week7: Nearest neighbors and VC-dimension week8: Case study week9: Mid-term presentation week10: Logistic regression week11: Support vector machine week12: Neural Networks week13: Generative Models week14: CNNs week15: Hidden Markov Models week16: LSTM and RNN week17: Case Study week18: Final exam

Evaluation and grading policy Homework 40% Mid-term project 30% Final Exam 30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
1. Hui Jiang, Machine learning Fundamentals A Concise Introduction, Cambridge, 2021 2.林大貴, 圖解TensorFlow 2初學篇：實作tf.keras + Colab雲端、深度學習、人工智慧、影像辨識, 博碩, 2022

The access to curricular consultation Email: shihwen@mail.ntut.edu.tw
Office Hours:
Wednesday 13:30-17:30
Integrate Technology Complex, Room B09-1

The course corresponds to the SDGs

Note
由於第17、18週改由線上教學，故原本規劃第17、18週期末報告改為線上報告。
Since the 17th and 18th weeks were changed to online teaching, the original planned final report for the 17th and 18th weeks was changed to an online report.

Instructor	Shih-Wen Chen　Office Hours
Email	shihwen@mail.ntut.edu.tw
Last Updated	2024-05-20 21:10:23
Course Objective	Introduction to machine learning by Python programming. Regression analysis, dimension reduction, feature extraction, classification methods, support vector machines, and neural networks. Python examples to improve students' knowledge of machine learning.
Course Schedule	week1: Introduction to machine learning week2: Introduction to python week3: Probability and optimization week4: Feature Extraction week5: Bayesian decision theory week6: Linear regression week7: Nearest neighbors and VC-dimension week8: Case study week9: Mid-term presentation week10: Logistic regression week11: Support vector machine week12: Neural Networks week13: Generative Models week14: CNNs week15: Hidden Markov Models week16: LSTM and RNN week17: Case Study week18: Final exam
Evaluation and grading policy	Homework 40% Mid-term project 30% Final Exam 30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 1. Hui Jiang, Machine learning Fundamentals A Concise Introduction, Cambridge, 2021 2.林大貴, 圖解TensorFlow 2初學篇：實作tf.keras + Colab雲端、深度學習、人工智慧、影像辨識, 博碩, 2022
The access to curricular consultation	Email: shihwen@mail.ntut.edu.tw Office Hours: Wednesday 13:30-17:30 Integrate Technology Complex, Room B09-1
The course corresponds to the SDGs
Note	由於第17、18週改由線上教學，故原本規劃第17、18週期末報告改為線上報告。 Since the 17th and 18th weeks were changed to online teaching, the original planned final report for the 17th and 18th weeks was changed to an online report.