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机器学习基石(台湾)

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机器学习基石(台湾)[65课时]

01-1 Course Introduction (10:41)

【免费试听】 01-2 What is Machine Learning (18:28)

【免费试听】 01-3 Applications of Machine Learning (18:57)

01-4 Components of Machine Learning (11:45)

01-5 Machine Learning and Other Fields (10:21)

02-1 Perceptron Hypothesis Set (15:25)

02-2 Perceptron Learning Algorithm (19:46)

02-3 Guarantee of PLA (12:37)

02-4 Non-Separable Data (12:55)

03-1 Learning with Different Output Space (17:9)

03-2 Learning with Different Data Label (18:12)

03-3 Learning with Different Protocol (11:9)

03-4 Learning with Different Input Space (14:13)

04-1 Learning is Impossible- (13:15)

04-2 Probability to the Rescue (11:32)

04-3 Connection to Learning (16:46)

04-4 Connection to Real Learning (18:5)

05-1 Recap and Preview (13:27)

05-2 Effective Number of Lines (15:26)

05-3 Effective Number of Hypotheses (16:17)

05-4 Break Point (7:44)

06-1 Restriction of Break Point (14:1)

06-2 Bounding Function- Basic Cases (6:56)

06-3 Bounding Function- Inductive Cases (14:46)

06-4 A Pictorial Proof (16:1)

07-1 Definition of VC Dimension (12:53)

07-2 VC Dimension of Perceptrons (13:27)

07-3 Physical Intuition of VC Dimension (6:10)

07-4 Interpreting VC Dimension (17:13)

08-1 Noise and Probabilistic Target (16:44)

08-2 Error Measure (15:10)

08-3 Algorithmic Error Measure (13:47)

08-4 Weighted Classification (16:54)

09-1 Linear Regression Problem (9:51)

09-2 Linear Regression Algorithm (20:2)

09-3 Generalization Issue (20:34)

09-4 Linear Regression for Binary Classification (11:23)

10-1 Logistic Regression Problem (14:15)

10-2 Logistic Regression Error (15:58)

10-3 Gradient of Logistic Regression Error (15:37)

10-4 Gradient Descent (19:18)

11-1 Linear Models for Binary Classification (21:18)

11-2 Stochastic Gradient Descent (11:39)

11-3 Multiclass via Logistic Regression (14:18)

11-4 Multiclass via Binary Classification (11:35)

12-1 Quadratic Hypothesis (23:29)

12-2 Nonlinear Transform (9:52)

12-3 Price of Nonlinear Transform (15:37)

12-4 Structured Hypothesis Sets (9:36)

13-1 What is Overfitting_ (10:28)

13-2 The Role of Noise and Data Size (13:35)

13-3 Deterministic Noise (14:7)

13-4 Dealing with Overfitting (10:49)

14-1 Regularized Hypothesis Set (18:59)

14-2 Weight Decay Regularization (24:8)

14-3 Regularization and VC Theory (8:15)

14-4 General Regularizers (13:28)

15-1 Model Selection Problem (15:43)

15-2 Validation (13:24)

15-3 Leave-One-Out Cross Validation (16:6)

15-4 V-Fold Cross Validation (10:41)

16-1 Occam-'s Razor (9:51)

16-2 Sampling Bias (11:50)

16-3 Data Snooping (12:28)

16-4 Power of Three (8:49)

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