Introduction to Machine Learning
机器学习导论系统介绍机器学习的基本概念、经典算法与理论基础。课程涵盖监督学习（线性回归、逻辑回归、决策树、支持向量机、集成学习）、无监督学习（聚类、降维、密度估计）以及模型评估、正则化、偏差-方差权衡等内容。学生将通过编程作业掌握算法实现，理解过拟合与泛化，为深入研究深度学习与统计学习打下基础。
Introduction to Machine Learning systematically covers fundamental concepts, classic algorithms, and theoretical foundations. Topics include supervised learning (linear and logistic regression, decision trees, support vector machines, ensemble methods), unsupervised learning (clustering, dimensionality reduction, density estimation), model evaluation, regularization, and the bias-variance trade-off. Programming assignments build implementation skills and intuition for generalization, preparing students for deep learning and statistical learning.
基于真实房屋交易数据集，完成特征工程、模型选择、调参与评估，对比多种算法表现，最终部署预测模型并撰写技术报告，分析模型可解释性。
Based on a real housing transaction dataset, complete feature engineering, model selection, tuning, and evaluation, compare multiple algorithms, deploy the prediction model, and write a technical report analyzing model interpretability.