Andrew Ng机器学习教程百度云地址

此课程将广泛介绍机器学习、数据挖掘与统计模式识别的知识。
(i) 监督学习（参数/非参数算法、支持向量机、内核、神经网络）。

(iii) 机器学习的优秀案例（偏差/方差理论；机器学习和人工智能视频的创新过程）课程将拮取案例研究与应用，学习如何将学习算法应用到智能机器人（观感，控制）、文字理解（网页搜索，防垃圾邮件）、计算机视觉、医学信息学、音频、数据挖掘及其他领域上。

1 - 1 - Welcome (7 min)

1 - 2 - What is Machine Learning- (7 min)

1 - 3 - Supervised Learning (12 min)

1 - 4 - Unsupervised Learning (14 min)

2 - 1 - Model Representation (8 min)

2 - 2 - Cost Function (8 min)

2 - 3 - Cost Function - Intuition I (11 min)

2 - 4 - Cost Function - Intuition II (9 min)

2 - 5 - Gradient Descent (11 min)

2 - 6 - Gradient Descent Intuition (12 min)

2 - 7 - Gradient Descent For Linear Regression (10 min)

2 - 8 - What-'s Next (6 min)

3 - 1 - Matrices and Vectors (9 min)

3 - 2 - Addition and Scalar Multiplication (7 min)

3 - 3 - Matrix Vector Multiplication (14 min)

3 - 4 - Matrix Matrix Multiplication (11 min)

3 - 5 - Matrix Multiplication Properties (9 min)

3 - 6 - Inverse and Transpose (11 min)

4 - 1 - Multiple Features (8 min)

4 - 2 - Gradient Descent for Multiple Variables (5 min)

4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min)

4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min)

4 - 5 - Features and Polynomial Regression (8 min)

4 - 6 - Normal Equation (16 min)

4 - 7 - Normal Equation Noninvertibility (Optional) (6 min)

5 - 1 - Basic Operations (14 min)

5 - 2 - Moving Data Around (16 min)

5 - 3 - Computing on Data (13 min)

5 - 4 - Plotting Data (10 min)

5 - 5 - Control Statements- for, while, if statements (13 min)

5 - 6 - Vectorization (14 min)

5 - 7 - Working on and Submitting Programming Exercises (4 min)

6 - 1 - Classification (8 min)

6 - 2 - Hypothesis Representation (7 min)

6 - 3 - Decision Bounry (15 min)

6 - 4 - Cost Function (11 min)

6 - 5 - Simplified Cost Function and Gradient Descent (10 min)

6 - 6 - Advanced Optimization (14 min)

6 - 7 - Multiclass Classification- One-vs-all (6 min)

7 - 1 - The Problem of Overfitting (10 min)

7 - 2 - Cost Function (10 min)

7 - 3 - Regularized Linear Regression (11 min)

7 - 4 - Regularized Logistic Regression (9 min)

8 - 1 - Non-linear Hypotheses (10 min)

8 - 2 - Neurons and the Brain (8 min)

8 - 3 - Model Representation I (12 min)

8 - 4 - Model Representation II (12 min)

8 - 5 - Examples and Intuitions I (7 min)

8 - 6 - Examples and Intuitions II (10 min)

8 - 7 - Multiclass Classification (4 min)

9 - 1 - Cost Function (7 min)

9 - 2 - Backpropagation Algorithm (12 min)

9 - 3 - Backpropagation Intuition (13 min)

9 - 4 - Implementation Note- Unrolling Parameters (8 min)

9 - 5 - Gradient Checking (12 min)

9 - 6 - Random Initialization (7 min)

9 - 7 - Putting It Together (14 min)

9 - 8 - Autonomous Driving (7 min)

10 - 1 - Deciding What to Try Next (6 min)

10 - 2 - Evaluating a Hypothesis (8 min)

10 - 3 - Model Selection and Train-Valition-Test Sets (12 min)

10 - 4 - Diagnosing Bias vs. Variance (8 min)

10 - 5 - Regularization and Bias-Variance (11 min)

10 - 6 - Learning Curves (12 min)

10 - 7 - Deciding What to Do Next Revisited (7 min)

11 - 1 - Prioritizing What to Work On (10 min)

11 - 2 - Error Analysis (13 min)

11 - 3 - Error Metrics for Skewed Classes (12 min)

11 - 4 - Trading Off Precision and Recall (14 min)

11 - 5 - Data For Machine Learning (11 min)

12 - 1 - Optimization Objective (15 min)

12 - 2 - Large Margin Intuition (11 min)

12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min)

12 - 4 - Kernels I (16 min)

12 - 5 - Kernels II (16 min)

12 - 6 - Using An SVM (21 min)

13 - 1 - Unsupervised Learning- Introduction (3 min)

13 - 2 - K-Means Algorithm (13 min)

13 - 3 - Optimization Objective (7 min)

13 - 4 - Random Initialization (8 min)

13 - 5 - Choosing the Number of Clusters (8 min)

14 - 1 - Motivation I- Data Compression (10 min)

14 - 2 - Motivation II- Visualization (6 min)

14 - 3 - Principal Component Analysis Problem Formulation (9 min)

14 - 4 - Principal Component Analysis Algorithm (15 min)

14 - 5 - Choosing the Number of Principal Components (11 min)

14 - 6 - Reconstruction from Compressed Representation (4 min)

14 - 7 - Advice for Applying PCA (13 min)

15 - 1 - Problem Motivation (8 min)

15 - 2 - Gaussian Distribution (10 min)

15 - 3 - Algorithm (12 min)

15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min)

15 - 5 - Anomaly Detection vs. Supervised Learning (8 min)

15 - 6 - Choosing What Features to Use (12 min)<br >