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

112-2 329021 人工智能機器學習 1 3.0 3 ★ 陳詩雯機電所
自動化所
機電博所
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

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
112-2	329021	人工智能機器學習	1	3.0	3	★	陳詩雯	機電所自動化所機電博所機電外國學生專班	53	3	機電所、自動化所、機電博和機電博外生班合開合開

教學大綱與進度：

教師姓名陳詩雯　教師諮商時間(Office Hours)

Email shihwen@mail.ntut.edu.tw

最後更新時間 2024-05-20 21:10:23

課程大綱 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.

課程進度 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

評量方式與標準 Homework 40% Mid-term project 30% Final Exam 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
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

課程諮詢管道 Email: shihwen@mail.ntut.edu.tw
Office Hours:
Wednesday 13:30-17:30
Integrate Technology Complex, Room B09-1

課程對應SDGs指標

備註
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.

教師姓名	陳詩雯　教師諮商時間(Office Hours)
Email	shihwen@mail.ntut.edu.tw
最後更新時間	2024-05-20 21:10:23
課程大綱	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.
課程進度	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
評量方式與標準	Homework 40% Mid-term project 30% Final Exam 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 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
課程諮詢管道	Email: shihwen@mail.ntut.edu.tw Office Hours: Wednesday 13:30-17:30 Integrate Technology Complex, Room B09-1
課程對應SDGs指標
備註	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.