Yaser-MachineLearning-CS156-Caltech

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yaser machinelearning cs156 caltech

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10/20/2018

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1/23/2022

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Files (54)

Yaser-MachineLearning-CS156-Caltech

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homework

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final.pdf

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final_sol.pdf

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hw1.pdf

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hw1_sol.pdf

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hw2.pdf

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hw4.pdf

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hw5.pdf

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hw6.pdf

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hw6_sol.pdf

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hw7.pdf

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hw7_sol.pdf

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hw8.pdf

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hw8_sol.pdf

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lectures

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Lecture 01 - The Learning Problem-mbyG85GZ0PI.mp4

Yaser-MachineLearning-CS156-Caltech/lectures/Lecture 01 - The Learning Problem-mbyG85GZ0PI.mp4

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Lecture 02 - Is Learning Feasible-MEG35RDD7RA.mp4

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Lecture 03 - The Linear Model I-FIbVs5GbBlQ.mp4

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Lecture 04 - Error and Noise-L_0efNkdGMc.mp4

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Lecture 05 - Training Versus Testing-SEYAnnLazMU.mp4

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Lecture 06 - Theory of Generalization-6FWRijsmLtE.mp4

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Lecture 07 - The VC Dimension-Dc0sr0kdBVI.mp4

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Lecture 08 - Bias-Variance Tradeoff-zrEyxfl2-a8.mp4

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Lecture 09 - The Linear Model II-qSTHZvN8hzs.mp4

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Lecture 10 - Neural Networks-Ih5Mr93E-2c.mp4

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Lecture 11 - Overfitting-EQWr3GGCdzw.mp4

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Lecture 12 - Regularization-I-VfYXzC5ro.mp4

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Lecture 13 - Validation-o7zzaKd0Lkk.mp4

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Lecture 14 - Support Vector Machines-eHsErlPJWUU.mp4

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Lecture 15 - Kernel Methods-XUj5JbQihlU.mp4

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Lecture 16 - Radial Basis Functions-O8CfrnOPtLc.mp4

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Lecture 17 - Three Learning Principles-EZBUDG12Nr0.mp4

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Lecture 18 - Epilogue-ihLwJPHkMRY.mp4

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slides

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slides01.pdf

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slides02.pdf

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slides11.pdf

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slides12.pdf

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slides13.pdf

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slides14.pdf

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slides15.pdf

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slides16.pdf

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slides17.pdf

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slides18.pdf

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