Course Code: datama
Duration: 28 hours
Overview:

目的：

代表们能够分析大数据集、提取模式、选择影响结果的正确变数，以便使用预测结果预测新模型。

Course Outline:

  1. 数据预处理

    1. Data Cleaning
    2. 数据集成和转换
    3. 数据缩减
    4. 离散化和概念层次结构生成

  2. 统计推断

    1. 概率分布、随机变数、中心极限定理
    2. 采样
    3. 置信区间
    4. 统计推断
    5. 假设检验

  3. 多元线性回归

    1. 规范
    2. 子集选择
    3. 估计
    4. 验证
    5. 预测

  4. 分类方法

    1. Logistic 回归
    2. 线性判别分析
    3. K - 最近邻
    4. 朴素贝叶斯
    5. 分类方法的比较

  5. Neural Networks

    1. 拟合神经网路
    2. 训练神经网路问题

  6. 决策树

    1. 回归树
    2. 分类树
    3. 树与线性模型

  7. 装袋， Random Forests， Boosting

    1. 装袋
    2. Random Forests
    3. 提高

  8. 支援向量机和柔性盘

    1. 最大边距分类器
    2. 支援向量分类器
    3. 支援向量机
    4. 2 个及以上类 SVM
    5. 与logistic回归的关系

  9. 主成分分析

  10. 聚类

    1. K-means 聚类
    2. K 中心点聚类
    3. 分层聚类
    4. 基于密度的聚类

  11. 模型评估和选择

    1. 偏差、方差和模型复杂性
    2. 样本内预测误差
    3. 贝叶斯方法
    4. 交叉验证
    5. Bootstrap 方法
Sites Published:

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