本书是一本专为经管类专业学生及从业者设计的人工智能入门教材。全书共 7 章，逻辑清晰，循序渐进。开篇从人工智能的学科溯源、哲学思辨与发展脉络入手，帮助读者建立宏观认知。随后，教材以Python 编程语言为基石，重点讲解了 NumPy、Pandas 等数据分析核心库，并深入介绍了数据可视化的方法与工具。本书系统讲解了机器学习与深度学习的基本原理、经典算法及其实践流程，并通过线性回归、逻辑回归、决策树、K-Means 聚类、卷积神经网络等丰富的综合案例，展示了如何将 AI 技术应用于财务分析、市场趋势预测、信用风险评估、客户分群等典型经管场景。 本书可作为高等院校经管类专业的本科生、研究生人工智能课程的教材，也可作为企业管理人员、数据分析师和对 AI 在商业领域应用感兴趣的读者的自学参考书。

谢玉枚，现任福建江夏学院电子信息科学学院计算机基础教研室副主任，主要负责Python编程基础课程的教学管理工作。在教学方面，秉持创新教育理念，深耕教学研究与改革，取得显著成效：主持福建省本科高校教育教学改革研究重大项目1项、教育部产学合作协同育人项目2项、新文科教育优秀教学案例1项、校级重点教改项目2项；主持建设省级线上线下混合式一流课程1门、省级继续教育精品课程1门、校级课程思政示范课程1门；参与编写出版教材2部。在科研方面，积极开展跨学科研究，主持省级自然科学基金面上项目1项、福建省中青年教育科研项目2项、福州高新区科技特派员项目1项、校级科研项目1项；发表学术论文5篇，获授权实用新型专利3项。

第 1 章 人工智能：溯源、现状与未来 ·····················································.001 1.1 人工智能概述 ·············································································.002 1.1.1 人工智能的学科定义与内涵 ···················································.003 1.1.2 智能的哲学思辨 ··································································.004 1.1.3 智能的多维度解析 ·······························································.004 1.2 人工智能发展脉络 ·······································································.006 1.2.1 奠基与孕育 ········································································.006 1.2.2 符号主义 ···········································································.008 1.2.3 第一次寒冬 ········································································.009 1.2.4 复兴之路 ···········································································.010 1.2.5 深度学习革命 ·····································································.012 1.2.6 大模型与生成式 AI ······························································.014 1.3 当代 AI 的技术与前沿 ··································································.015 1.3.1 核心技术框架 ·····································································.016 1.3.2 产业生态结构 ·····································································.017 1.3.3 前沿研究方向 ·····································································.018 1.3.4 典型应用场景 ·····································································.020 1.4 本章小结 ···················································································.021 1.5 本章练习 ···················································································.021 第 2 章 AI 编程基石：Python 快速入门 ···················································.025 2.1 Python 语言概述 ··········································································.026 2.1.1 发展历程 ···········································································.026 2.1.2 核心特性与生态 ··································································.027 2.1.3 Python 环境搭建与配置 ·························································.028 2.2 Python 基础语法 ··········································································.030 2.2.1 基本输入和输出 ··································································.030 V 人工智能基础应用（经管类）
2.2.2 程序的格式框架 ··································································.031 2.2.3 变量、标识符与关键字 ·························································.032 2.2.4 基本数据类型 ·····································································.033 2.2.5 运算符和表达式 ··································································.037 2.2.6 常用内置函数与字符串方法 ···················································.039 2.3 程序流程控制 ·············································································.042 2.3.1 顺序结构 ···········································································.042 2.3.2 选择结构 ···········································································.043 2.3.3 循环结构 ···········································································.046 2.3.4 异常处理 ···········································································.050 2.4 组合数据类型 ·············································································.052 2.4.1 列表 ·················································································.052 2.4.2 元组 ·················································································.055 2.4.3 集合 ·················································································.057 2.4.4 字典 ·················································································.060 2.5 函数与模块化编程 ·······································································.062 2.5.1 模块化编程思想 ··································································.062 2.5.2 函数的定义与调用 ·······························································.063 2.5.3 参数传递 ···········································································.064 2.5.4 变量的作用域 ·····································································.065 2.5.5 lambda 函数 ·······································································.066 2.5.6 递归函数 ···········································································.066 2.5.7 函数分类与常用函数 ····························································.067 2.6 面向对象编程 ·············································································.070 2.6.1 类与对象的概念 ··································································.070 2.6.2 类的定义与对象创建 ····························································.070 2.7 文件读写与一维/二维数据处理 ·······················································.072 2.7.1 文件基础 ···········································································.072 2.7.2 不同维度数据处理 ·······························································.073 2.8 本章小结 ···················································································.074 2.9 本章练习 ···················································································.075 第 3 章 NumPy：高效数值计算 ····························································.080 3.1 NumPy 概述 ···············································································.081 3.1.1 NumPy 简介 ·······································································.081 VI
目 录
3.1.2 应用领域概览 ·····································································.083 3.2 NumPy 核心功能详解 ···································································.084 3.2.1 数组创建与基础操作 ····························································.084 3.2.2 数学运算与广播机制 ····························································.089 3.2.3 统计分析与聚合计算 ····························································.093 3.3 NumPy 高级操作与应用 ·······························································.094 3.3.1 数组结构重塑 ·····································································.094 3.3.2 数组合并与拆分 ··································································.095 3.3.3 高级索引与筛选 ··································································.098 3.3.4 数值型数据预处理 ·······························································.099 3.4 本章小结 ···················································································.101 3.5 本章练习 ···················································································.101 第 4 章 Pandas：数据科学工具 ·····························································.104 4.1 Pandas 概述 ················································································.105 4.1.1 Pandas 核心优势与应用生态 ···················································.106 4.1.2 Pandas 核心架构 ··································································.107 4.2 Pandas 核心数据结构 ···································································.108 4.2.1 Series：一维带标签数组 ·······················································.108 4.2.2 DataFrame：二维表格型数据 ·················································.110 4.3 数据操作与处理（基础篇） ···························································.113 4.3.1 数据读写 ···········································································.113 4.3.2 索引机制与数据访问 ····························································.115 4.3.3 数据清洗：重复值处理与缺失值处理 ·······································.120 4.3.4 数据整合与数据转换 ····························································.125 4.4 Pandas 数据分析实战（进阶篇） ·····················································.130 4.4.1 数据排序、排名与采样 ·························································.130 4.4.2 时间序列处理 ·····································································.132 4.4.3 描述性统计与数据探索 ·························································.134 4.4.4 数据分组与聚合统计 ····························································.135 4.4.5 多维数据透视分析 ·······························································.136 4.4.6 数据分布特征与相关性分析 ···················································.137 4.5 本章小结 ···················································································.140 4.6 本章练习 ···················································································.140 VII 人工智能基础应用（经管类）
第 5 章 数据可视化：从数据到洞察 ························································.143 5.1 数据可视化概述 ··········································································.144 5.2 Matplotlib 可视化 ·········································································.145 5.2.1 安装 Matplotlib ···································································.145 5.2.2 pyplot 基础语法 ··································································.146 5.2.3 常见图表类型与绘制方法 ······················································.151 5.3 Seaborn 高级统计可视化 ································································.155 5.3.1 Seaborn 简介 ······································································.156 5.3.2 Seaborn 的核心绘图类别 ·······················································.156 5.4 经管数据可视化实践 ····································································.159 5.4.1 财务数据可视化 ··································································.159 5.4.2 市场趋势可视化 ··································································.162 5.5 本章小结 ···················································································.165 5.6 本章练习 ···················································································.165 第 6 章 机器学习：从理论到应用 ···························································.167 6.1 机器学习概述 ·············································································.168 6.1.1 机器学习的定义 ··································································.168 6.1.2 监督学习与无监督学习 ·························································.170 6.1.3 机器学习一般流程 ·······························································.173 6.2 机器学习实践 ·············································································.175 6.2.1 Scikit-learn 简介 ··································································.175 6.2.2 数据预处理 ········································································.176 6.2.3 模型构建与训练 ··································································.181 6.2.4 模型评估 ···········································································.182 6.2.5 模型调优与交叉验证 ····························································.186 6.3 综合实践案例 ·············································································.187 6.4 本章小结 ···················································································.204 6.5 本章练习 ···················································································.204 第 7 章 神经网络：从入门到应用 ···························································.207 7.1 人工神经元 ················································································.208 7.1.1 神经元模型 ········································································.208 7.1.2 激活函数 ···········································································.210 VIII
目 录
7.2 神经网络 ···················································································.212 7.2.1 神经网络的基本结构 ····························································.212 7.2.2 神经网络训练过程 ·······························································.213 7.2.3 神经网络简单实例 ·······························································.215 7.2.4 深度神经网络 ·····································································.218 7.3 深度学习 ···················································································.218 7.3.1 深度学习定义 ·····································································.219 7.3.2 深度学习模型 ·····································································.219 7.3.3 深度学习简单实例 ·······························································.224 7.4 本章小结 ···················································································.228 7.5 本章练习 ···················································································.228