Course Code: datama
Duration: 28 hours
Overview:

目的：

代表們能夠分析大數據集、提取模式、選擇影響結果的正確變數，以便使用預測結果預測新模型。

Course Outline:

  1. 數據預處理

    1. Data Cleaning
    2. 數據集成和轉換
    3. 數據縮減
    4. 離散化和概念層次結構生成

  2. 統計推斷

    1. 概率分佈、隨機變數、中心極限定理
    2. 採樣
    3. 置信區間
    4. 統計推斷
    5. 假設檢驗

  3. 多元線性回歸

    1. 規範
    2. 子集選擇
    3. 估計
    4. 驗證
    5. 預測

  4. 分類方法

    1. Logistic 回歸
    2. 線性判別分析
    3. K - 最近鄰
    4. 樸素貝葉斯
    5. 分類方法的比較

  5. Neural Networks

    1. 擬合神經網路
    2. 訓練神經網路問題

  6. 決策樹

    1. 回歸樹
    2. 分類樹
    3. 樹與線性模型

  7. 裝袋， Random Forests， Boosting

    1. 裝袋
    2. Random Forests
    3. 提高

  8. 支援向量機和柔性盤

    1. 最大邊距分類器
    2. 支援向量分類器
    3. 支援向量機
    4. 2 個及以上類 SVM
    5. 與logistic回歸的關係

  9. 主成分分析

  10. 聚類

    1. K-means 聚類
    2. K 中心點聚類
    3. 分層聚類
    4. 基於密度的聚類

  11. 模型評估和選擇

    1. 偏差、方差和模型複雜性
    2. 樣本內預測誤差
    3. 貝葉斯方法
    4. 交叉驗證
    5. Bootstrap 方法
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