ESS 4270-0

Brief

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.

Description

Course keywords: 機率, 分類, 回歸分析, 監督式, 非監督式, probability, classification, regression analysis, supervised, unsupervised 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. Decision Trees 11. Linear Discrimination 12. Multilayer Perceptrons 13. Deep Learning 六、成績考核(Evaluation) 1. Homework & Computer Assignments 25% 2. Two Midterm Exams 50% 3. Class project competition (Presentation + Report + Competition ranking) 25% No make-up exam!!

Timetable

Remarks

本課程上150分鐘，其餘時間由教授彈性運用

Restrictions

限工科系

Compulsory

Elective

Cross Discipline

First Specialization

Second Specialization