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)