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�γ��°벿�֣�http://www.santongit.com/thread-30292-1-1.html
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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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