graph learn {
    NN [label="神經網路"];
    SL [label="統計學習"];
    LA [label="線性代數"];
    Calc [label="微積分"];
    P [label="機率"];
    S [label="統計"];
    Alg [label="演算法"];
    NN -- {LA, Calc, Alg};
    SL -- {LA, Calc, P, S, Alg};
}

graph NN_graph {
    NN [label="神經網路"];
    FF [label="Feedforward"];
    BP [label="Backpropagation"];
    NN -- {FF, BP};
    
    matrix [label="矩陣運算"];
    vtize [label="向量化計算"];
    grad [label="梯度"];
    taylor [label="泰勒展開"];
    FF -- {matrix, vtize};
    BP -- {matrix, vtize, grad, taylor};
}

graph RF_graph {
    RF [label="隨機森林"];
    par [label="分割"];
    bag [label="bagging"];
    RF -- {par, bag};

    LG [label="線性幾何"];
    ent [label="資訊熵"];
    sample [label="抽樣"];
    par -- {LG, ent};
    bag -- {sample};
}

graph PCA_graph {
    PCA [label="主成份分析 PCA"];
    varmtx [label="變異數矩陣"];
    PC [label="主成份萃取"];
    PCA -- {varmtx, PC};
    
    LG [label="線性幾何"];
    mean [label="平均值"];
    var [label="變異數"];
    SVD [label="奇異值分解 SVD"];
    eig [label="對角化"];
    varmtx -- {mean, var, LG};
    PC -- {LG, var, SVD};
    SVD -- eig;
}

graph core {
    layout=twopi;
    ranksep=2.5;
    AIMATH [label="AI & Math"];
    Python [label="Python 與機器學習之理論實現"];
    AI [label="人工智慧原理"];
    LA [label="線性代數"];
    Calc [label="微積分"];
    P [label="機率"];
    S [label="統計"];
    Alg [label="計算機程式"];
    AIMATH -- {LA, Calc, P, S, Alg, AI, Python};

}