课程信息

本课程将以Python为工具，介绍基本的机器学习算法，同时涵盖监督式和非监督式学习的概念和应用。通过这些算法的学习和实作，同学将能够创建数据分析和模式识别的基础能力。本课程介绍的非监督式学习算法包括阶层式分群法（Hierarchical Clustering）、K-平均算法（K-means）、K最近邻法（KNN）、决策树（Decision Trees）、单纯贝氏分类器（Naïve Bayes）、DBSCAN、高斯混合模型（GMM）和线性回归（Linear Regression）。通过这些主题的学习和实作，同学将能够使用Python实作这些机器学习算法，并能够应用于真实世界的数据分析和模式识别任务中。本课程将结合理论知识和实际练习，帮助同学创建机器学习的基础。同学可了解每个算法的原理，通过实作练习，培养同学的问题解决能力和实际应用能力。

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.