一、課程說明(Course Description)
This course introduces basic concepts in machine learning and their associated
mathematical tools. It is aimed at advanced undergraduates, and assumes no
previous knowledge of or machine learning concepts. Knowledge of multivariate
calculus, basic linear algebra some familiarity with probabilities would be
helpful but not essential. Topics to be covered include probability
distributions, supervised learning, Bayesian decision theory, parametric
methods, multivariate methods, dimensionality reduction, clustering,
nonparametric methods, decision trees, linear discrimination, multilayer
perceptrons, and deep learning.
二、指定用書(Text Books)
Introduction to Machine Learning, Ethem Alpaydin, 3rd ed. 2014, The MIT Press.
三、參考書籍(References)
1. Applied Statistics and Probability for Engineers, Douglas C.
Montgomery, 6th ed. 2014, John Wiley & Sons.
四、教學方式(Teaching Method)
課堂授課,每周上課 150 分鐘
五、教學進度 (Syllabus)
1. Introduction
2. Math Background
3. Supervised Learning
4. Bayesian Decision Theory
5. Parametric Methods
6. Multivariate Methods
7. Dimensionality Reduction
8. Clustering
9. Nonparametric Methods
10. Linear Discrimination
11. Multilayer Perceptron
12. Deep Learning
六、成績考核(Evaluation)
1. Computer Assignments 40%
2. Personal Project (Oral Presentation + Report) 35%
3. Class Competition (Competition ranking) 25%
No make-up exam!!
Student use of the AI: Conditionally open; please specify how generative AI will
be used in course output.