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Location:
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Machine Learning/Algorithm
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The cornerstone of machine learning
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The Role of Noise and Data Size
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The Role of Noise and Data Size
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Playlist
Chapters
Chapter 1:Course Introduction
Chapter 2:What is Machine Learning
Chapter 3:Applications of Machine Learning
Chapter 4:Components of Machine Learning
Chapter 5:Machine Learning and Other Fields
Chapter 6:Perceptron Hypothesis Set
Chapter 7:Perceptron Learning Algorithm (PLA)
Chapter 8:Guarantee of PLA
Chapter 9:Non-Separable Data
Chapter 10:Learning with Different Output Space
Chapter 11:Learning with Different Data Label
Chapter 12:Learning with Different Protocol
Chapter 13:Learning with Different Input Space
Chapter 14:Learning is Impossible
Chapter 15:Probability to the Rescue
Chapter 16:Connection to Learning
Chapter 17:Connection to Real Learning
Chapter 18:Recap and Preview
Chapter 19:Effective Number of Lines
Chapter 20:Effective Number of Hypotheses
Chapter 21:Break Point
Chapter 22:Restriction of Break Point
Chapter 23:Bounding Function- Basic Cases
Chapter 24:Bounding Function- Inductive Cases
Chapter 25:A Pictorial Proof
Chapter 26:Definition of VC Dimension
Chapter 27:VC Dimension of Perceptrons
Chapter 28:Physical Intuition of VC Dimension
Chapter 29:Interpreting VC Dimension
Chapter 30:Noise and Probabilistic Target
Chapter 31:Error Measure
Chapter 32:Algorithmic Error Measure
Chapter 33:Weighted Classification
Chapter 34:Linear Regression Problem
Chapter 35:Linear Regression Algorithm
Chapter 36:Generalization Issue
Chapter 37:Linear Regression for Binary Classification
Chapter 38:Logistic Regression Problem
Chapter 39:Logistic Regression Error
Chapter 40:Gradient of Logistic Regression Error
Chapter 41:Gradient Descent
Chapter 42:Linear Models for Binary Classification
Chapter 43:Stochastic Gradient Descent
Chapter 44:Multiclass via Logistic Regression
Chapter 45:Multiclass via Binary Classification
Chapter 46:Quadratic Hypothesis
Chapter 47:Nonlinear Transform
Chapter 48:Price of Nonlinear Transform
Chapter 49:Structured Hypothesis Sets
Chapter 50:What is Overfitting
Chapter 51:The Role of Noise and Data Size
Chapter 52:Deterministic Noise
Chapter 53:Dealing with Overfitting
Chapter 54:Regularized Hypothesis Set
Chapter 55:Weight Decay Regularization
Chapter 56:Regularization and VC Theory
Chapter 57:General Regularizers
Chapter 58:Model Selection Problem
Chapter 59:Validation
Chapter 60:Leave-One-Out Cross Validation
Chapter 61:V-Fold Cross Validation
Chapter 62:Occam-'s Razor
Chapter 63:Sampling Bias
Chapter 64:Data Snooping
Chapter 65:Power of Three
Duration:13 minutes and 36 seconds
Date:2018/04/06
Uploader:老白菜
Introduction
keywords:
Machine Learning
Lin Xuantian
Introduces basic algorithms, theories and practical tools that machine learning users in various fields should know.
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