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

——————-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