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)
发表于 2017-3-24 21:17:14
