The cornerstone of machine learning

Introduces basic algorithms, theories and practical tools that machine learning users in various fields should know.

• Chapter 1：Course Introduction (10 minutes and 58 seconds)

• Chapter 2：What is Machine Learning (18 minutes and 28 seconds)

• Chapter 3：Applications of Machine Learning (18 minutes and 56 seconds)

• Chapter 4：Components of Machine Learning (11 minutes and 45 seconds)

• Chapter 5：Machine Learning and Other Fields (10 minutes and 21 seconds)

• Chapter 6：Perceptron Hypothesis Set (15 minutes and 42 seconds)

• Chapter 7：Perceptron Learning Algorithm (PLA) (19 minutes and 46 seconds)

• Chapter 8：Guarantee of PLA (12 minutes and 37 seconds)

• Chapter 9：Non-Separable Data (12 minutes and 55 seconds)

• Chapter 10：Learning with Different Output Space (17 minutes and 26 seconds)

• Chapter 11：Learning with Different Data Label (18 minutes and 12 seconds)

• Chapter 12：Learning with Different Protocol (11 minutes and 9 seconds)

• Chapter 13：Learning with Different Input Space (14 minutes and 13 seconds)

• Chapter 14：Learning is Impossible (13 minutes and 32 seconds)

• Chapter 15：Probability to the Rescue (11 minutes and 33 seconds)

• Chapter 16：Connection to Learning (16 minutes and 46 seconds)

• Chapter 17：Connection to Real Learning (18 minutes and 6 seconds)

• Chapter 18：Recap and Preview (13 minutes and 44 seconds)

• Chapter 19：Effective Number of Lines (15 minutes and 26 seconds)

• Chapter 20：Effective Number of Hypotheses (16 minutes and 17 seconds)

• Chapter 21：Break Point (7 minutes and 44 seconds)

• Chapter 22：Restriction of Break Point (14 minutes and 18 seconds)

• Chapter 23：Bounding Function- Basic Cases (6 minutes and 56 seconds)

• Chapter 24：Bounding Function- Inductive Cases (14 minutes and 47 seconds)

• Chapter 25：A Pictorial Proof (16 minutes and 1 seconds)

• Chapter 26：Definition of VC Dimension (13 minutes and 10 seconds)

• Chapter 27：VC Dimension of Perceptrons (13 minutes and 27 seconds)

• Chapter 28：Physical Intuition of VC Dimension (6 minutes and 11 seconds)

• Chapter 29：Interpreting VC Dimension (17 minutes and 13 seconds)

• Chapter 30：Noise and Probabilistic Target (17 minutes and 1 seconds)

• Chapter 31：Error Measure (15 minutes and 10 seconds)

• Chapter 32：Algorithmic Error Measure (13 minutes and 46 seconds)

• Chapter 33：Weighted Classification (16 minutes and 54 seconds)

• Chapter 34：Linear Regression Problem (10 minutes and 8 seconds)

• Chapter 35：Linear Regression Algorithm (20 minutes and 2 seconds)

• Chapter 36：Generalization Issue (20 minutes and 34 seconds)

• Chapter 37：Linear Regression for Binary Classification (11 minutes and 23 seconds)

• Chapter 38：Logistic Regression Problem (14 minutes and 33 seconds)

• Chapter 39：Logistic Regression Error (15 minutes and 58 seconds)

• Chapter 40：Gradient of Logistic Regression Error (15 minutes and 38 seconds)

• Chapter 41：Gradient Descent (19 minutes and 18 seconds)

• Chapter 42：Linear Models for Binary Classification (21 minutes and 35 seconds)

• Chapter 43：Stochastic Gradient Descent (11 minutes and 39 seconds)

• Chapter 44：Multiclass via Logistic Regression (14 minutes and 18 seconds)

• Chapter 45：Multiclass via Binary Classification (11 minutes and 35 seconds)

• Chapter 46：Quadratic Hypothesis (23 minutes and 47 seconds)

• Chapter 47：Nonlinear Transform (9 minutes and 52 seconds)

• Chapter 48：Price of Nonlinear Transform (15 minutes and 37 seconds)

• Chapter 49：Structured Hypothesis Sets (9 minutes and 36 seconds)

• Chapter 50：What is Overfitting (10 minutes and 45 seconds)

• Chapter 51：The Role of Noise and Data Size (13 minutes and 36 seconds)

• Chapter 52：Deterministic Noise (14 minutes and 7 seconds)

• Chapter 53：Dealing with Overfitting (10 minutes and 49 seconds)

• Chapter 54：Regularized Hypothesis Set (19 minutes and 16 seconds)

• Chapter 55：Weight Decay Regularization (24 minutes and 8 seconds)

• Chapter 56：Regularization and VC Theory (8 minutes and 15 seconds)

• Chapter 57：General Regularizers (13 minutes and 28 seconds)

• Chapter 58：Model Selection Problem (16 minutes and 0 seconds)

• Chapter 59：Validation (13 minutes and 24 seconds)

• Chapter 60：Leave-One-Out Cross Validation (16 minutes and 6 seconds)

• Chapter 61：V-Fold Cross Validation (10 minutes and 41 seconds)

• Chapter 62：Occam-'s Razor (10 minutes and 8 seconds)

• Chapter 63：Sampling Bias (11 minutes and 50 seconds)

• Chapter 64：Data Snooping (12 minutes and 28 seconds)

• Chapter 65：Power of Three (8 minutes and 49 seconds)