课时1：Course Introduction

课时2：What is Machine Learning

课时3：Applications of Machine Learning

课时4：Components of Machine Learning

课时5：Machine Learning and Other Fields

课时6：Perceptron Hypothesis Set

课时7：Perceptron Learning Algorithm (PLA)

课时8：Guarantee of PLA

课时9：Non-Separable Data

课时10：Learning with Different Output Space

课时11：Learning with Different Data Label

课时12：Learning with Different Protocol

课时13：Learning with Different Input Space

课时14：Learning is Impossible

课时15：Probability to the Rescue

课时16：Connection to Learning

课时17：Connection to Real Learning

课时18：Recap and Preview

课时19：Effective Number of Lines

课时20：Effective Number of Hypotheses

课时21：Break Point

课时22：Restriction of Break Point

课时23：Bounding Function- Basic Cases

课时24：Bounding Function- Inductive Cases

课时25：A Pictorial Proof

课时26：Definition of VC Dimension

课时27：VC Dimension of Perceptrons

课时28：Physical Intuition of VC Dimension

课时29：Interpreting VC Dimension

课时30：Noise and Probabilistic Target

课时31：Error Measure

课时32：Algorithmic Error Measure

课时33：Weighted Classification

课时34：Linear Regression Problem

课时35：Linear Regression Algorithm

课时36：Generalization Issue

课时37：Linear Regression for Binary Classification

课时38：Logistic Regression Problem

课时39：Logistic Regression Error

课时40：Gradient of Logistic Regression Error

课时41：Gradient Descent

课时42：Linear Models for Binary Classification

课时43：Stochastic Gradient Descent

课时44：Multiclass via Logistic Regression

课时45：Multiclass via Binary Classification

课时46：Quadratic Hypothesis

课时47：Nonlinear Transform

课时48：Price of Nonlinear Transform

课时49：Structured Hypothesis Sets

课时50：What is Overfitting

课时51：The Role of Noise and Data Size

课时52：Deterministic Noise

课时53：Dealing with Overfitting

课时54：Regularized Hypothesis Set

课时55：Weight Decay Regularization

课时56：Regularization and VC Theory

课时57：General Regularizers

课时58：Model Selection Problem

课时59：Validation

课时60：Leave-One-Out Cross Validation

课时61：V-Fold Cross Validation

课时62：Occam-'s Razor

课时63：Sampling Bias

课时64：Data Snooping

课时65：Power of Three