課程資訊

本課程將以Python為工具，介紹基本的機器學習演算法，同時涵蓋監督式和非監督式學習的概念和應用。透過這些演算法的學習和實作，同學將能夠建立數據分析和模式識別的基礎能力。本課程介紹的非監督式學習演算法包括階層式分群法（Hierarchical Clustering）、K-平均演算法（K-means）、K最近鄰法（KNN）、決策樹（Decision Trees）、單純貝氏分類器（Naïve Bayes）、DBSCAN、高斯混合模型（GMM）和線性回歸（Linear Regression）。透過這些主題的學習和實作，同學將能夠使用Python實作這些機器學習演算法，並能夠應用於真實世界的數據分析和模式識別任務中。本課程將結合理論知識和實際練習，幫助同學建立機器學習的基礎。同學可瞭解每個演算法的原理，通過實作練習，培養同學的問題解決能力和實際應用能力。

This course will use Python as a tool to introduce basic machine learning algorithms, covering both supervised and unsupervised learning concepts and applications. Through learning and practical implementation of these algorithms, students will be able to establish a foundation in data analysis and pattern recognition. The unsupervised learning algorithms introduced in this course include Hierarchical Clustering, K-means, K-nearest neighbors (KNN), Decision Trees, Naïve Bayes, DBSCAN, Gaussian Mixture Model (GMM), and Linear Regression. By learning and implementing these topics, students will be able to use Python to implement these machine learning algorithms and apply them to real-world data analysis and pattern recognition tasks. This course will combine theoretical knowledge and practical exercises to help students build a foundation in machine learning. Students will gain an understanding of the principles behind each algorithm and develop problem-solving and practical application skills through hands-on practice.