清华大学 龙星计划 机器学习课程

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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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