机器学习基石培训 台大讲师林轩田 机器学习基础入门培训视频教程 机器学习课程

机器学习基石培训 台大讲师林轩田 机器学习基础入门培训视频教程 机器学习课程

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——————-课程目录——————-
01_handout.pdf
02_handout.pdf
03_handout.pdf
04_handout.pdf
05_handout.pdf
06_handout.pdf
07_handout.pdf
08_handout.pdf
09_handout.pdf
1 – 1 – Course Introduction (10-58).mp4
1 – 2 – What is Machine Learning (18-28).mp4
1 – 3 – Applications of Machine Learning (18-56).mp4
1 – 4 – Components of Machine Learning (11-45).mp4
1 – 5 – Machine Learning and Other Fields (10-21).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).mp4
10_handout.pdf
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
11_handout.pdf
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
12_handout.pdf
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
HomeWork1.doc
homework2.docx
homework3.docx

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