8月6日-10日,9:00-13:45,清华大学,北京
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Day 1

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)

Day 2

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)

Day 3

lecture 9: overview of learning theory (tong)

lecture 10: optimization in machine learning (tong)

lecture 11: online learning (tong)

lecture 12: sparsity models (tong)

Day 4

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)

Day 5

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)