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	326123	機器學習	1	3.0	3	★	尤信程	資工四	22	1	電資外、資工四和資工所合開

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	2024-05-15 14:58:12
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: introduction to machine learning (cont'd) Week 3: Basics of supervised learning (classification and regression), VC dimension, Bayesian decision theory, and Naive Bayes classifiers Week 4: Dimension reduction techniques: PCA, FA, LDA and ICA, Week 5: Unsupervised learning: Clustering algorithm (k-means) Week 6: Decision trees: ID3, C4.5, and bagging algorithm (random forest) Week 7: Basics of optimization Week 8: Basics of optimization (Cont'd) and gradient search Week 9: MT Week 10: MT sol. 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 Week 15: SVM, Adaboost, and Ensemble learning Week 16: Final exam Week 17: Presentation Week 18: Presentation
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 Course PPT files are available for students.
課程諮詢管道	Office: Tech building 1533 Email: scyou@ntut.edu.tw
課程對應SDGs指標
備註	In case online lecturing is required by CDC, we will use the Microsoft Teams as the lecturing platform. E-mail me if you have any problems: scyou@ntut.edu.tw 4/2 & 4/8 class meetings will be held as usual. Lectures on Week 17 & 18 will be held on Microsoft Teams.