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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)
