[{"title":"( 27 个子文件 50.53MB ) 清华大学-学堂在线-大数据机器学习课件笔记.zip","children":[{"title":"19 深度学习正则化方法.pdf <span style='color:#111;'> 3.23MB </span>","children":null,"spread":false},{"title":"13.1 em算法拓展.pdf <span style='color:#111;'> 2.57MB </span>","children":null,"spread":false},{"title":"10 核函数与非线性svm.pdf <span style='color:#111;'> 2.07MB </span>","children":null,"spread":false},{"title":"6 贝叶斯分类器和概率图模型.pdf <span style='color:#111;'> 1.38MB </span>","children":null,"spread":false},{"title":"12 提升方法adaboost算法.pdf <span style='color:#111;'> 1.45MB </span>","children":null,"spread":false},{"title":"11 降维与度量学习.pdf <span style='color:#111;'> 2.78MB </span>","children":null,"spread":false},{"title":"16 条件随机场.pdf <span style='color:#111;'> 1.84MB </span>","children":null,"spread":false},{"title":"5 聚类.pdf <span style='color:#111;'> 2.39MB </span>","children":null,"spread":false},{"title":"12.1 adaboost补充.pdf <span style='color:#111;'> 2.25MB </span>","children":null,"spread":false},{"title":"1 概述.pdf <span style='color:#111;'> 3.12MB </span>","children":null,"spread":false},{"title":"4 感知机.pdf <span style='color:#111;'> 873.23KB </span>","children":null,"spread":false},{"title":"9.1 支持向量机补充.pdf <span style='color:#111;'> 3.84MB </span>","children":null,"spread":false},{"title":"8 逻辑斯蒂logistic回归与最大熵模型.pdf <span style='color:#111;'> 1.27MB </span>","children":null,"spread":false},{"title":"8.1 logistic算法补充.pdf <span style='color:#111;'> 2.23MB </span>","children":null,"spread":false},{"title":"9 支持向量机svm.pdf <span style='color:#111;'> 959.04KB </span>","children":null,"spread":false},{"title":"20 深度学习优化方法.pdf <span style='color:#111;'> 2.28MB </span>","children":null,"spread":false},{"title":"13 EM算法及混合高斯模型.pdf <span style='color:#111;'> 1.59MB </span>","children":null,"spread":false},{"title":"2 机器学习基本概念.pdf <span style='color:#111;'> 1.61MB </span>","children":null,"spread":false},{"title":"7 决策树与随机森林.pdf <span style='color:#111;'> 1.56MB </span>","children":null,"spread":false},{"title":"7.1 决策树补充.pdf <span style='color:#111;'> 2.74MB </span>","children":null,"spread":false},{"title":"3 模型性能评估.pdf <span style='color:#111;'> 2.23MB </span>","children":null,"spread":false},{"title":"6.1 贝叶斯分类器应用补充.pdf <span style='color:#111;'> 2.41MB </span>","children":null,"spread":false},{"title":"4.1 感知机补充.pdf <span style='color:#111;'> 1.36MB </span>","children":null,"spread":false},{"title":"14 计算学习理论.pdf <span style='color:#111;'> 2.41MB </span>","children":null,"spread":false},{"title":"18 神经网络与深度学习.pdf <span style='color:#111;'> 1.82MB </span>","children":null,"spread":false},{"title":"15 隐马尔可夫模型和概率图模型.pdf <span style='color:#111;'> 1.27MB </span>","children":null,"spread":false},{"title":"17 概率图模型的学习与推断.pdf <span style='color:#111;'> 1.46MB </span>","children":null,"spread":false}],"spread":true}]