機器學習

臺北科技大學 114-2 學期課程，課號 359811。

課程大綱

This course introduces students to the process of data science, covering problem formulation, data acquisition, modeling and analysis, and presentation and integration into action. Students will learn useful skills as well as basic statistical methods by completing several hands-on problems focused on real-world data. This course starts with a brief introduction to multivariate data analysis and data summarization including visualization. Then it extends to focus on using data to develop predictive capabilities using supervised and unsupervised machine learning models (regression, classification, clustering, PCA, etc.).

課程進度

Week 1 Data summarization and visualization Week 2-4 Multivariate normal distribution Week 5-6 Principal component analysis Week 7-8 Naïve Bayes, Neural networks Week 9 Exam or assignment Week 10-14 Supervised learning (Regression, Classification, Decision tree, kNN, SVM, Ensemble method – Bagging, RF, Boosting) Week 15-17 Unsupervised learning (Clustering, k-means) Week 18 Exam or assignment

評量方式

1. Final Exam or Team Project: 40 % 2. Assignment: 50 % 3. Attendance: 10 %

教材

【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：是 Textbook: ‘Applied Multivariate Statistical Analysis’ written by Richard A. Johnson and Dean W. Wichern. Supplementary references: 'Machine Learning (1997)' written by Tom Mitchell, ‘Introduction to Machine Learning with Python: A Guide for Data Scientists’ written by Andreas C. Müller and Sarah Guido