TA的每日心情 | 擦汗 2023-5-6 02:41 |
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签到天数: 570 天 [LV.9]以坛为家II
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目录
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)
│ Lecture01.mp4
│ Lecture02.mp4
│ Lecture03.mp4
│ Lecture04.mp4
│ Lecture05.mp4
│ Lecture06.mp4
│ Lecture07.mp4
│ Lecture08.mp4
│ Lecture09.mp4
│ Lecture10.mp4
│ Lecture11.mp4
│ Lecture12.mp4
│ Lecture13.mp4
│ Lecture14.mp4
│ Lecture15.mp4
│ Lecture16.mp4
│ Lecture17.mp4
│ Lecture18.mp4
│ Lecture19.mp4
│ mulu.txt
│
└─资料
1.dragonstar_lecture1_intro.pdf
10.optimization.pdf
11.online.pdf
12.sparsity.pdf
13.dragonstar_graphical_model.pdf
14.dragonstar_lecture14_structured_learning.pdf
15.dragonstar_deep.pdf
16.dragonstar_transfer_semi.pdf
17.dragonstar_recommendation.pdf
18.dragonstar_vision.pdf
19.lecture19.pdf
2.lecture02.pdf
3.lecture03.pdf
4.dragonstar_lecture4_linear_classification.pdf
5.dragonstar_lecture5_nonlinear_svm.pdf
6.dragonstar_lecture6_model_selection.pdf
7.lecture07.pdf
8.lecture08.pdf
9.lecture09.pdf
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