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龙星计划《机器学习》

2017/10/18
124
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本课程以直播录播课件的形式播放,欢迎小伙伴们随时提问,我们整理大家的问题,为下次的课程打下基础。

lecture 1: Introduction to ML and review of linear algebra, probability, statistics (kai)

lecture 2: linear model (tong)

lecture 3: overfitting and regularization (tong)

lecture 4: linear classification (kai)

lecture 5: basis expansion and kernel methods (kai)

lecture 6: model selection and evaluation (kai)

lecture 7: model combination (tong)

lecture 8: boosting and bagging (tong)

lecture 9: overview of learning theory (tong)

lecture 10: optimization in machine learning (tong)

lecture 11: online learning (tong)

lecture 12: sparsity models (tong)

lecture 13: introduction to graphical models (kai)

lecture 14: structured learning (kai)

lecture 15: feature learning and deep learning (kai)

lecture 16: transfer learning and semi supervised learning (kai)

lecture 17: matrix factorization and recommendations (kai)

lecture 18: learning on images (kai)

lecture 19: learning on the web (tong)

lecture 20: summary and road ahead (tong) 

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