1 - 1 - Course Introduction (10-58)
1 - 2 - What is Machine Learning (18-28)
1 - 3 - Applications of Machine Learning (18-56)
1 - 4 - Components of Machine Learning (11-45)
1 - 5 - Machine Learning and Other Fields (10-21)
2 - 1 - Perceptron Hypothesis Set (15-42)
2 - 2 - Perceptron Learning Algorithm (PLA) (19-46)
2 - 3 - Guarantee of PLA (12-37)
2 - 4 - Non-Separable Data (12-55)
3 - 1 - Learning with Different Output Space (17-26)
3 - 2 - Learning with Different Data Label (18-12)
3 - 3 - Learning with Different Protocol (11-09)
3 - 4 - Learning with Different Input Space (14-13)
4 - 1 - Learning is Impossible- (13-32)
4 - 2 - Probability to the Rescue (11-33)
4 - 3 - Connection to Learning (16-46)
4 - 4 - Connection to Real Learning (18-06)
5 - 1 - Recap and Preview (13-44)
5 - 2 - Effective Number of Lines (15-26)
5 - 3 - Effective Number of Hypotheses (16-17)
5 - 4 - Break Point (07-44)
6 - 1 - Restriction of Break Point (14-18)
6 - 3 - Bounding Function- Inductive Cases (14-47)
6 - 4 - A Pictorial Proof (16-01)
7 - 1 - Definition of VC Dimension (13-10)
7 - 2 - VC Dimension of Perceptrons (13-27)
7 - 3 - Physical Intuition of VC Dimension (6-11)
7 - 4 - Interpreting VC Dimension (17-13)
8 - 1 - Noise and Probabilistic Target (17-01)
8 - 3 - Algorithmic Error Measure (13-46)
8 - 4 - Weighted Classification (16-54)
9 - 1 - Linear Regression Problem (10-08)
9 - 2 - Linear Regression Algorithm (20-03)
9 - 3 - Generalization Issue (20-34)
9 - 4 - Linear Regression for Binary Classification (11-23)
10 - 1 - Logistic Regression Problem (14-33)
10 - 2 - Logistic Regression Error (15-58)
10 - 3 - Gradient of Logistic Regression Error (15-38)
10 - 4 - Gradient Descent (19-18)
11 - 1 - Linear Models for Binary Classification (21-35)
11 - 2 - Stochastic Gradient Descent (11-39)
11 - 3 - Multiclass via Logistic Regression (14-18)
11 - 4 - Multiclass via Binary Classification (11-35)
12 - 1 - Quadratic Hypothesis (23-47)
12 - 2 - Nonlinear Transform (09-52)
12 - 3 - Price of Nonlinear Transform (15-37)
12 - 4 - Structured Hypothesis Sets (09-36)
13 - 1 - What is Overfitting- (10-45)
13 - 2 - The Role of Noise and Data Size (13-36)
13 - 3 - Deterministic Noise (14-07)
13 - 4 - Dealing with Overfitting (10-49)
14 - 1 - Regularized Hypothesis Set (19-16)
14 - 2 - Weight Decay Regularization (24-08)
14 - 3 - Regularization and VC Theory (08-15)
14 - 4 - General Regularizers (13-28)
15 - 1 - Model Selection Problem (16-00)
15 - 2 - Validation (13-24)
15 - 3 - Leave-One-Out Cross Validation (16-06)
15 - 4 - V-Fold Cross Validation (10-41)
16 - 1 - Occam-'s Razor (10-08)
16 - 2 - Sampling Bias (11-50)
16 - 3 - Data Snooping (12-28)
16 - 4 - Power of Three (08-49)
