RedstoneWill / Hsuantienlin Ml Camp
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HsuanTienLin-ML-Camp
课程资料
-
李航《统计学习方法》(链接：https://pan.baidu.com/s/1MSx407RuPCJt5KSej0Yqlg 密码：h74l)
-
周志华《机器学习》（链接：https://pan.baidu.com/s/1wyqhvJHkI1zHph8RRsm9iw 密码：1475）
教学大纲
整体按照林轩田基石和技法的课程顺序来进行，总课时12周。
第1周 何时机器可以学习？
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机器学习问题
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学习回答Yes/No
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机器学习类型
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机器学习的可行性
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作业1
第2周 为什么机器可以学习？
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训练vs测试
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泛化理论
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VC维度
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噪声与误差
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作业2
第3周 机器如何学习？
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线性回归
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逻辑回归
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线性分类模型
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非线性特征转换
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作业3
第4周 机器如何更好地学习？
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过拟合的危险性
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正则化
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模型验证
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三大学习原则
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作业4
第5周 带打天池o2o实战赛（初级）
第6周 支持向量机SVM
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线性支持向量机
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对偶支持向量机
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核支持向量机
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软间隔支持向量机
第7周 SVM 核技巧的推广
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核逻辑回归
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支持向量回归
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作业5
第8周 SMO基本原理与实现
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SMO算法的基本原理
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Python实现SMO算法
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数字手写识别
第9周 集成学习Bagging和AdaBoost
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Blending和Bagging
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提升算法AdaBoost
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决策树
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作业6
第10周 随机森林和GBDT
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随机森林
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GBDT算法
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作业7
第11周 神经网络
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神经网络
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深度学习
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RBF网络
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矩阵提取
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作业8
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总结
第12周 带打天池o2o实战赛（进阶）
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