国立台湾大学林轩田机器学习基石网课资源

国立台湾大学林轩田机器学习基石网课资源 百度云下载

路径：IT1区/ –00新课/ 2022 / 34.七月在线机器学习/机器学习基石_国立台湾大学(林轩田)

国立台湾大学林轩田机器学习基石网课资源 百度云目录：

1 – 1 – Course Introduction (10-58)(1).mp4

1 – 2 – What is Machine Learning (18-28).mp4

1 – 3 – Applications of Machine Learning (18-56)(1).mp4

1 – 4 – Components of Machine Learning (11-45)(1).mp4

1 – 5 – Machine Learning and Other Fields (10-21)(1).mp4

2 – 1 – Perceptron Hypothesis Set (15-42).mp4

2 – 2 – Perceptron Learning Algorithm (PLA) (19-46).mp4

2 – 3 – Guarantee of PLA (12-37).mp4

2 – 4 – Non-Separable Data (12-55).mp4

3 – 1 – Learning with Different Output Space (17-26).mp4

3 – 2 – Learning with Different Data Label (18-12).mp4

3 – 3 – Learning with Different Protocol (11-09).mp4

3 – 4 – Learning with Different Input Space (14-13).mp4

4 – 1 – Learning is Impossible- (13-32).mp4

4 – 2 – Probability to the Rescue (11-33).mp4

4 – 3 – Connection to Learning (16-46).mp4

4 – 4 – Connection to Real Learning (18-06).mp4

5 – 1 – Recap and Preview (13-44).mp4

5 – 2 – Effective Number of Lines (15-26).mp4

5 – 3 – Effective Number of Hypotheses (16-17).mp4

5 – 4 – Break Point (07-44).mp4

6 – 1 – Restriction of Break Point (14-18).mp4

6 – 2 – Bounding Function- Basic Cases (06-56).mp4

6 – 3 – Bounding Function- Inductive Cases (14-47).mp4

6 – 4 – A Pictorial Proof (16-01).mp4

7 – 1 – Definition of VC Dimension (13-10).mp4

7 – 2 – VC Dimension of Perceptrons (13-27).mp4

7 – 3 – Physical Intuition of VC Dimension (6-11).mp4

7 – 4 – Interpreting VC Dimension (17-13).mp4

8 – 1 – Noise and Probabilistic Target (17-01).mp4

8 – 2 – Error Measure (15-10).mp4

8 – 3 – Algorithmic Error Measure (13-46).mp4

8 – 4 – Weighted Classification (16-54).mp4

9 – 1 – Linear Regression Problem (10-08).mp4

9 – 2 – Linear Regression Algorithm (20-03).mp4

9 – 3 – Generalization Issue (20-34).mp4

9 – 4 – Linear Regression for Binary Classification (11-23).mp4

10 – 1 – Logistic Regression Problem (14-33).mp4

10 – 2 – Logistic Regression Error (15-58).mp4

10 – 3 – Gradient of Logistic Regression Error (15-38).mp4

10 – 4 – Gradient Descent (19-18)(1).mp4

11 – 1 – Linear Models for Binary Classification (21-35).mp4

11 – 2 – Stochastic Gradient Descent (11-39).mp4

11 – 3 – Multiclass via Logistic Regression (14-18).mp4

11 – 4 – Multiclass via Binary Classification (11-35).mp4

12 – 1 – Quadratic Hypothesis (23-47).mp4

12 – 2 – Nonlinear Transform (09-52).mp4

12 – 3 – Price of Nonlinear Transform (15-37).mp4

12 – 4 – Structured Hypothesis Sets (09-36).mp4

13 – 1 – What is Overfitting- (10-45).mp4

13 – 2 – The Role of Noise and Data Size (13-36).mp4

13 – 3 – Deterministic Noise (14-07).mp4

13 – 4 – Dealing with Overfitting (10-49).mp4

14 – 1 – Regularized Hypothesis Set (19-16).mp4

14 – 2 – Weight Decay Regularization (24-08).mp4

14 – 3 – Regularization and VC Theory (08-15).mp4

14 – 4 – General Regularizers (13-28).mp4

15 – 1 – Model Selection Problem (16-00).mp4

15 – 2 – Validation (13-24).mp4

15 – 3 – Leave-One-Out Cross Validation (16-06).mp4

15 – 4 – V-Fold Cross Validation (10-41).mp4

16 – 1 – Occam-‘s Razor (10-08).mp4

16 – 2 – Sampling Bias (11-50).mp4

16 – 3 – Data Snooping (12-28).mp4

16 – 4 – Power of Three (08-49).mp4

需要请添加qq296792825（微信同号）

或扫下方二维码添加微信获取：