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	326123	Machine Learning	1	3.0	3	★	SHINGCHERN YOU	4CSIE4	22	1	電資外、資工四和資工所合開

Course Plan：

Instructor	SHINGCHERN YOU　Office Hours
Email	scyou@ntut.edu.tw
Last Updated	2024-05-15 14:58:12
Course Objective	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)
Course Schedule	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
Evaluation and grading policy	MT 30 % Final 40 % HW 30 % Project 10 % (optional)
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No Course PPT files are available for students.
The access to curricular consultation	Office: Tech building 1533 Email: scyou@ntut.edu.tw
The course corresponds to the SDGs
Does the course incorporate AI
Note	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.