1 - 1 - Course Introduction (10-58)

学员 1 - 2 - What is Machine Learning (18-28)

学员 1 - 3 - Applications of Machine Learning (18-56)

学员 1 - 4 - Components of Machine Learning (11-45)

学员 1 - 5 - Machine Learning and Other Fields (10-21)

学员 2 - 1 -Perceptron Hypothesis Set (15-42)

学员 2 - 2 -Perceptron Learning Algorithm (PLA) (19-46)

学员 2 - 3 - Guarantee of PLA (12-37)

学员 2 - 4 - Non-Separable Data (12-55)

学员 3 - 1 - Learning with Different Output Space (17-26)

学员 3 - 2 - Learning with Different Data Label (18-12)

学员 3 - 3 - Learning with Different Protocol (11-09)

学员 3 - 4 - Learning with Different Input Space (14-13)

学员 4 - 1 - Learning is Impossible- (13-32)

学员 4 - 2 - Probability to the Rescue (11-33)

学员 4 - 3 - Connection to Learning (16-46)

学员 4 - 4 -Connection to real learning

学员 5 - 1 - Recap and Preview (13-44)

学员 5 - 2 - Effective Number of Lines (15-26)

学员 5 - 3 - Effective Number of Hypotheses (16-17)

学员 5 - 4 - Break Point (07-44)

学员 6 - 1 - Restriction of Break Point (14-18)

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

学员 6 - 3 - Bounding Function- Inductive Cases (14-47)

学员 6 - 4 - A Pictorial Proof (16-01)

学员 7 - 1 - Definition of VC Dimension (13-10)

学员 7 - 2 - VC Dimension of Perceptrons (13-27)

学员 7 - 3 - Physical Intuition of VC Dimension (6-11)

学员 7 - 4 - Interpreting VC Dimension (17-13)

学员 8 - 1 - Noise and Probabilistic Target (17-01)