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

111-2 316604 機器學習 1 3.0 3 ★ 尤信程資工所
電資外生所 34 4 資工所和電資外生所合開

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
111-2	316604	機器學習	1	3.0	3	★	尤信程	資工所電資外生所	34	4	資工所和電資外生所合開

教學大綱與進度：

教師姓名尤信程　教師諮商時間(Office Hours)

Email scyou@ntut.edu.tw

最後更新時間 2022-12-16 13:38:18

課程大綱 This course covers a broad classes of machine learning algorithms. After taking this course, students should be able to conduct research in this field. The emphasis of this course is not in the theoretical development (math), but in the understanding level. Therefore, many examples will be given in the lecture to show how to apply the equations to real problems. The detailed contents are given in the course schedule field. Prerequisite: Calculus (partial derivatives), undergrad linear algebra (eigenvalues & eigenvectors), undergrad probability (random variable, joint density, Gaussian distribution), and programming skills (Python)

課程進度 Week 1: Class announcement and introduction to machine learning Week 2: Holiday & introduction to machine learning Week 3: Basics of supervised learning (classification and regression), VC dimension, Bayesian decision theory, and Naive Bayes classifiers Week 4: ML and MAP estimation, Sample mean and sample covariance, multivariate Gaussian, Bias vs variance dilemma, Dimension reduction techniques: PCA, FA Week 5: Dimension reduction techniques: LDA and ICA, Unsupervised learning: Clustering algorithm (k-means) Week 6: Decision trees: ID3, C4.5, and bagging algorithm (random forest) Week 7: Holiday Week 8: Basics of optimization Week 9: MT Week 10: MT sol. Gradient search and linear discrimination Week 11: Feedforward neural networks with examples: multi-layer perceptrons, Back propagation, and Regularization methods Week 12: Deep learning and convolutional neural networks Week 13, 14: More on deep neural networks: YOLO, Autoencoder, LSTM, and advanced topics, such as Bayesian networks, confidence of classification, and self-supervised learning (if time permitted) Week 15: SVM Week 16: Adaboost and Ensemble learning Week 17: Design and analysis of experiments Week 18: Final exam

評量方式與標準 MT 30 % Final 40 % HW 30 % Project 10 % (optional)

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：否
Reference text book: Introduction to machine learning. E. Alpaydin. 2nd ed or 3rd ed. Note: textbook is only used to follow the presentation order. Much of the detailed lecture materials are NOT covered in the textbook.

課程諮詢管道 Office: Tech building 1533
Email: scyou@ntut.edu.tw

備註
In case online lecturing is required by CDC, we will use the Microsoft Teams as the lecturing platform (NTUT-Sync]110-2_298550_機器學習). E-mail me if you have any problems: scyou@ntut.edu.tw

教師姓名	尤信程　教師諮商時間(Office Hours)
Email	scyou@ntut.edu.tw
最後更新時間	2022-12-16 13:38:18
課程大綱	This course covers a broad classes of machine learning algorithms. After taking this course, students should be able to conduct research in this field. The emphasis of this course is not in the theoretical development (math), but in the understanding level. Therefore, many examples will be given in the lecture to show how to apply the equations to real problems. The detailed contents are given in the course schedule field. Prerequisite: Calculus (partial derivatives), undergrad linear algebra (eigenvalues & eigenvectors), undergrad probability (random variable, joint density, Gaussian distribution), and programming skills (Python)
課程進度	Week 1: Class announcement and introduction to machine learning Week 2: Holiday & introduction to machine learning Week 3: Basics of supervised learning (classification and regression), VC dimension, Bayesian decision theory, and Naive Bayes classifiers Week 4: ML and MAP estimation, Sample mean and sample covariance, multivariate Gaussian, Bias vs variance dilemma, Dimension reduction techniques: PCA, FA Week 5: Dimension reduction techniques: LDA and ICA, Unsupervised learning: Clustering algorithm (k-means) Week 6: Decision trees: ID3, C4.5, and bagging algorithm (random forest) Week 7: Holiday Week 8: Basics of optimization Week 9: MT Week 10: MT sol. Gradient search and linear discrimination Week 11: Feedforward neural networks with examples: multi-layer perceptrons, Back propagation, and Regularization methods Week 12: Deep learning and convolutional neural networks Week 13, 14: More on deep neural networks: YOLO, Autoencoder, LSTM, and advanced topics, such as Bayesian networks, confidence of classification, and self-supervised learning (if time permitted) Week 15: SVM Week 16: Adaboost and Ensemble learning Week 17: Design and analysis of experiments Week 18: Final exam
評量方式與標準	MT 30 % Final 40 % HW 30 % Project 10 % (optional)
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：否 Reference text book: Introduction to machine learning. E. Alpaydin. 2nd ed or 3rd ed. Note: textbook is only used to follow the presentation order. Much of the detailed lecture materials are NOT covered in the textbook.
課程諮詢管道	Office: Tech building 1533 Email: scyou@ntut.edu.tw
備註	In case online lecturing is required by CDC, we will use the Microsoft Teams as the lecturing platform (NTUT-Sync]110-2_298550_機器學習). E-mail me if you have any problems: scyou@ntut.edu.tw