Andrew Ng机器学习课程目录(带翻译)

I. Introduction (Week 1)一、引言（第1周）•Welcome (7 min)•欢迎（7分钟）•What is Machine Learning? (7 min)•机器学习是什么？（7分钟）•Supervised Learning (12 min)•监督学习（12分钟）•Unsupervised Learning (14 min)•无监督学习（14分钟）II. Linear Regression with One Variable (Week 1)二、一元线性回归（第1周）•Model Representation (8 min)•模型表示（8分钟）•Cost Function (8 min)•成本函数（8分钟）•Cost Function - Intuition I (11 min)•成本函数的直观理解I（11分钟）•Cost Function - Intuition II (9 min)•成本函数的直观理解II（9分钟）•Gradient Descent (11 min)•梯度下降（11分钟）•Gradient Descent Intuition (12 min)•梯度下降的直观理解（12分钟）•Gradient Descent For Linear Regression (10 min) •梯度下降的线性回归（10分钟）•What's Next (6 min)•接下来的内容（6分钟）III. Linear Algebra Review (Week 1, Optional)三、线性代数回顾（第1周，可选）•Matrices and Vectors (9 min)•矩阵和向量（9分钟）•Addition and Scalar Multiplication (7 min)•加法和标量乘法（7分钟）•Matrix Vector Multiplication (14 min)•矩阵向量乘法（14分钟）•Matrix MatrixMultiplication (11 min)•矩阵乘法（11分钟）•Matrix Multiplication Properties (9 min)•矩阵乘法的性质（9分钟）•Inverse and Transpose (11 min)•逆、转置（11分钟）IV. Linear Regression with Multiple Variables (Week 2)四、多变量线性回归（第2周）•Multiple Features (8 min)•多特征（8分钟）•Gradient Descent for Multiple Variables (5 min)•梯度下降的多变量（5分钟）•Gradient Descent in Practice I - Feature Scaling (9 min) •梯度下降法实践1-特征尺度（9分钟）•Gradient Descent in Practice II - Learning Rate (9 min)•梯度下降法实践2-学习速率（9分钟）•Features and Polynomial Regression (8 min)•特征和多项式回归（8分钟）•Normal Equation (16 min)•正规方程（16分钟）•Normal Equation Noninvertibility (Optional) (6 min)•正规方程不可逆性（可选）（6分钟）V. Octave Tutorial (Week 2)五、Octave教程（第2周）•Basic Operations (14 min)•基本操作（14分钟）•Moving Data Around (16 min)•移动数据（16分钟）•Computing on Data (13 min)•计算数据（13分钟）•Plotting Data (10 min)•绘图数据（10分钟）•Control Statements: for, while, if statements (13 min)•控制语句：for，while，if语句（13分钟）•Vectorization (14 min)•矢量化（14分钟）•Working on and Submitting Programming Exercises (4 min) •工作和提交的编程练习（4分钟）VI. Logistic Regression (Week 3)六、Logistic回归（第3周）•Classification (8 min)•分类（8分钟）•Hypothesis Representation (7 min)•假说表示（7分钟）•Decision Boundary (15 min)•决策边界（15分钟）•Cost Function (11 min)•成本函数（11分钟）•Simplified Cost Function and Gradient Descent (10 min) •简化的成本函数和梯度下降（10分钟）•Advanced Optimization (14 min)•Multiclass Classification: One-vs-all (6 min)•多类分类：一个对所有（6分钟）VII. Regularization (Week 3)七、正则化（第3周）•The Problem of Overfitting (10 min)•过拟合的问题（10分钟）•Cost Function (10 min)•成本函数（10分钟）•Regularized Linear Regression (11 min)•正则线性回归（11分钟）•Regularized Logistic Regression (9 min)•正则化的逻辑回归模型（9分钟）VIII. Neural Networks: Representation (Week 4)第八、神经网络：表述（第4周）•Non-linear Hypotheses (10 min)•非线性假设（10分钟）•Neurons and the Brain (8 min)•神经元和大脑（8分钟）•Model Representation I (12 min)•模型表示1（12分钟）•Model Representation II (12 min)•模型表示2（12分钟）•Examples and Intuitions I (7 min)•例子和直观理解1（7分钟）•Examples and Intuitions II (10 min)•例子和直观理解II（10分钟）•Multiclass Classification (4 min)•多类分类（4分钟）IX. Neural Networks: Learning (Week 5)九、神经网络的学习（第5周）•Cost Function (7 min)•成本函数（7分钟）•Backpropagation Algorithm (12 min)•反向传播算法（12分钟）•Backpropagation Intuition (13 min)•反向传播算法直观理解（13分钟）•Implementation Note: Unrolling Parameters (8 min) •实现注意：展开参数（8分钟）•Gradient Checking (12 min)•梯度检测（12分钟）•Random Initialization (7 min)•随机初始化（7分钟）•Putting It Together (14 min)•Autonomous Driving (7 min)•自主驾驶（7分钟）X. Advice for Applying Machine Learning (Week 6)十、应用机器学习的建议（第6周）•Deciding What to Try Next (6 min)•决定下一步做什么（6分钟）•Evaluating a Hypothesis (8 min)•评估一个假设（8分钟）•Model Selection and Train/Validation/Test Sets (12 min)•模型的选择和培训/测试集（12分钟）•Diagnosing Bias vs. Variance (8 min)•诊断偏差和方差（8分钟）•Regularization and Bias/Variance (11 min)•正则化和偏差/方差（11分钟）•Learning Curves (12 min)•学习曲线（12分钟）•Deciding What to Do Next Revisited (7 min)•决定下一步做什么（7分钟）XI. Machine Learning System Design (Week 6)十一、机器学习系统的设计（第6周）•Prioritizing What to Work On (10 min)•优先要做什么（10分钟）•Error Analysis (13 min)•误差分析（13分钟）•Error Metrics for Skewed Classes (12 min)•误差度量的偏斜类别（12分钟）•Trading Off Precision and Recall (14 min)•交易精度和召回（14分钟）•Data For Machine Learning (11 min)•数据的机器学习（11分钟）XII. Support Vector Machines (Week 7)十二、支持向量机（第7周）•Optimization Objective (15 min)•优化目标（15分钟）•Large Margin Intuition (11 min)•大边界的直观理解（11分钟）•Mathematics Behind Large Margin Classification (Optional) (20 min) •数学背后的大边界分类（可选）（20分钟）•Kernels I (16 min)核函数1（16分钟）•Kernels II (16 min)•核函数2（16分钟）•Using An SVM (21 min)•使用支持向量机（21分钟）XIII. Clustering (Week 8)十三、聚类（第8周）•Unsupervised Learning: Introduction (3 min)•无监督的学习：简介（3分钟）•K-Means Algorithm (13 min)•k-均值算法（13分钟）•Optimization Objective (7 min)•优化目标（7分钟）•Random Initialization (8 min)•随机初始化（8分钟）•Choosing the Number of Clusters (8 min)•选择簇的数量（8分钟）XIV. Dimensionality Reduction (Week 8)十四、降维（8周）•Motivation I: Data Compression (10 min)•动机1：数据压缩（10分钟）•Motivation II: Visualization (6 min)•动机2：可视化（6分钟）•Principal Component Analysis Problem Formulation (9 min)•主成分分析问题（9分钟）•Principal Component Analysis Algorithm (15 min)•主成分分析算法（15分钟）•Choosing the Number of Principal Components (11 min)•从主成分选择数（11分钟）•Reconstruction from Compressed Representation (4 min)•重建的压缩表示（4分钟）•Advice for Applying PCA (13 min)•主成分分析法的应用建议（13分钟）XV. Anomaly Detection (Week 9)十五、异常检测（第9周）•Problem Motivation (8 min)•问题的动机（8分钟）•Gaussian Distribution (10 min)•高斯分布（10分钟）•Algorithm (12 min)•算法（12分钟）•Developing and Evaluating an Anomaly Detection System (13 min) •开发和评价一个异常检测系统（13分钟）•Anomaly Detection vs. Supervised Learning (8 min)•异常检测与监督学习（8分钟）•Choosing What Features to Use (12 min)•选择使用什么特征（12分钟）•Multivariate Gaussian Distribution (Optional) (14 min)•多元高斯分布（可选）（14分钟）•Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min) 使用多变量的高斯分布进行异常检测（可选）（14分钟）XVI. Recommender Systems (Week 9)十六、推荐系统（第9周）•Problem Formulation (8 min)•问题（8分钟）•Content Based Recommendations (15 min)•基于内容的推荐（15分钟）•Collaborative Filtering (10 min)•协同过滤（10分钟）•Collaborative Filtering Algorithm (9 min)•协同过滤算法（9分钟）•Vectorization: Low Rank Matrix Factorization (8 min)•矢量化：低秩矩阵分解（8分钟）•Implementational Detail: Mean Normalization (9 min)•推行工作上的细节：均值归一化（9分钟）XVII. Large Scale Machine Learning (Week 10)十七、大规模机器学习（第10周）•Learning With Large Datasets (6 min)大型数据集的学习•（6分钟）•Stochastic Gradient Descent (13 min)•随机梯度下降（13分钟）•Mini-Batch Gradient Descent (6 min)•迷你批处理梯度下降（6分钟）•Stochastic Gradient Descent Convergence (12 min)•随机梯度下降法收敛（12分钟）•Online Learning (13 min)•在线学习（13分钟）•Map Reduce and Data Parallelism (14 min)•减少图和数据并行（14分钟）XVIII. Application Example: Photo OCR十八、应用实例：照片的OCR•Problem Description and Pipeline (7 min)•问题描述和传播途径（7分钟）•Sliding Windows (15 min)•滑动窗口（15分钟）•Getting Lots of Data and Artificial Data (16 min)•获取数据和大量人造数据（16分钟）•Ceiling Analysis: What Part of the Pipeline to Work on Next (14 min)•上限分析：哪部分管道的接下去做（14分钟）XIX. Conclusion十九、结论•Summary and Thank You (5 min)•总结和致谢（5分钟）。