Machine Learning Andrew Ng

This course will provide a broad introduction to machine learning, data mining and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Excellent cases of machine learning (bias/variance theory; innovation process of machine learning and artificial intelligence) course will focus on case studies and applications to learn how to apply learning algorithms to intelligent robots (perception, control), text understanding ( Web search, anti-spam), computer vision, medical informatics, audio, data mining and other fields.

• Chapter 1：Welcome (6 minutes and 54 seconds)

• Chapter 2：What is Machine Learning (7 minutes and 14 seconds)

• Chapter 3：Supervised Learning (12 minutes and 29 seconds)

• Chapter 4：Unsupervised Learning (14 minutes and 13 seconds)

• Chapter 5：Model Representation (8 minutes and 10 seconds)

• Chapter 6：Cost Function (8 minutes and 12 seconds)

• Chapter 7：Cost Function - Intuition I (11 minutes and 9 seconds)

• Chapter 8：Cost Function - Intuition II (8 minutes and 48 seconds)

• Chapter 9：Gradient Descent (11 minutes and 30 seconds)

• Chapter 10：Gradient Descent Intuition (11 minutes and 50 seconds)

• Chapter 11：Gradient Descent For Linear Regression (10 minutes and 20 seconds)

• Chapter 12：What-'s Next (5 minutes and 49 seconds)

• Chapter 13：Matrices and Vectors (8 minutes and 45 seconds)

• Chapter 14：Addition and Scalar Multiplication (6 minutes and 53 seconds)

• Chapter 15：Matrix Vector Multiplication (13 minutes and 39 seconds)

• Chapter 16：Matrix Matrix Multiplication (11 minutes and 9 seconds)

• Chapter 17：Matrix Multiplication Properties (9 minutes and 2 seconds)

• Chapter 18：Inverse and Transpose (11 minutes and 12 seconds)

• Chapter 19：Multiple Features (8 minutes and 22 seconds)

• Chapter 20：Gradient Descent for Multiple Variables (5 minutes and 4 seconds)

• Chapter 21：Gradient Descent in Practice I - Feature Scaling (8 minutes and 51 seconds)

• Chapter 22：Gradient Descent in Practice II - Learning Rate (8 minutes and 58 seconds)

• Chapter 23：Features and Polynomial Regression (7 minutes and 39 seconds)

• Chapter 24：Normal Equation (16 minutes and 17 seconds)

• Chapter 25：Normal Equation Noninvertibility (Optional) (5 minutes and 58 seconds)

• Chapter 26：Basic Operations (13 minutes and 59 seconds)

• Chapter 27：Moving Data Around (16 minutes and 7 seconds)

• Chapter 28：Computing on Data (13 minutes and 14 seconds)

• Chapter 29：Plotting Data (9 minutes and 38 seconds)

• Chapter 30：Control Statements- for, while, if statements (12 minutes and 55 seconds)

• Chapter 31：Vectorization (13 minutes and 48 seconds)

• Chapter 32：Working on and Submitting Programming Exercises (3 minutes and 33 seconds)

• Chapter 33：Classification (8 minutes and 8 seconds)

• Chapter 34：Hypothesis Representation (7 minutes and 24 seconds)

• Chapter 35：Decision Boundary (14 minutes and 49 seconds)

• Chapter 36：Cost Function (11 minutes and 25 seconds)

• Chapter 37：Simplified Cost Function and Gradient Descent (10 minutes and 14 seconds)

• Chapter 38：Advanced Optimization (14 minutes and 6 seconds)

• Chapter 39：Multiclass Classification- One-vs-all (6 minutes and 15 seconds)

• Chapter 40：The Problem of Overfitting (9 minutes and 42 seconds)

• Chapter 41：Cost Function (10 minutes and 10 seconds)

• Chapter 42：Regularized Linear Regression (10 minutes and 40 seconds)

• Chapter 43：Regularized Logistic Regression (8 minutes and 33 seconds)

• Chapter 44：Non-linear Hypotheses (9 minutes and 35 seconds)

• Chapter 45：Neurons and the Brain (7 minutes and 47 seconds)

• Chapter 46：Model Representation I (12 minutes and 1 seconds)

• Chapter 47：Model Representation II (11 minutes and 46 seconds)

• Chapter 48：Examples and Intuitions I (7 minutes and 15 seconds)

• Chapter 49：Examples and Intuitions II (10 minutes and 20 seconds)

• Chapter 50：Multiclass Classification (3 minutes and 51 seconds)

• Chapter 51：Cost Function (6 minutes and 43 seconds)

• Chapter 52：Backpropagation Algorithm (11 minutes and 59 seconds)

• Chapter 53：Backpropagation Intuition (12 minutes and 44 seconds)

• Chapter 54：Implementation Note- Unrolling Parameters (7 minutes and 47 seconds)

• Chapter 55：Gradient Checking (11 minutes and 37 seconds)

• Chapter 56：Random Initialization (6 minutes and 51 seconds)

• Chapter 57：Putting It Together (13 minutes and 23 seconds)

• Chapter 58：Autonomous Driving (6 minutes and 30 seconds)

• Chapter 59：Deciding What to Try Next (5 minutes and 50 seconds)

• Chapter 60：Evaluating a Hypothesis (7 minutes and 35 seconds)

• Chapter 61：Model Selection and Train-Validation-Test Sets (12 minutes and 3 seconds)

• Chapter 62：Diagnosing Bias vs. Variance (7 minutes and 42 seconds)

• Chapter 63：Regularization and Bias-Variance (11 minutes and 20 seconds)

• Chapter 64：Learning Curves (11 minutes and 53 seconds)

• Chapter 65：Deciding What to Do Next Revisited (6 minutes and 50 seconds)

• Chapter 66：Prioritizing What to Work On (9 minutes and 29 seconds)

• Chapter 67：Error Analysis (13 minutes and 11 seconds)

• Chapter 68：Error Metrics for Skewed Classes (11 minutes and 35 seconds)

• Chapter 69：Trading Off Precision and Recall (14 minutes and 5 seconds)

• Chapter 70：Data For Machine Learning (11 minutes and 9 seconds)

• Chapter 71：Optimization Objective (14 minutes and 47 seconds)

• Chapter 72：Large Margin Intuition (10 minutes and 36 seconds)

• Chapter 73：Mathematics Behind Large Margin Classification (Optional) (19 minutes and 41 seconds)

• Chapter 74：Kernels I (15 minutes and 44 seconds)

• Chapter 75：Kernels II (15 minutes and 43 seconds)

• Chapter 76：Using An SVM (21 minutes and 2 seconds)

• Chapter 77：Unsupervised Learning- Introduction (3 minutes and 16 seconds)

• Chapter 78：K-Means Algorithm (12 minutes and 32 seconds)

• Chapter 79：Optimization Objective (7 minutes and 4 seconds)

• Chapter 80：Random Initialization (7 minutes and 49 seconds)

• Chapter 81：Choosing the Number of Clusters (8 minutes and 22 seconds)

• Chapter 82：Motivation I- Data Compression (10 minutes and 9 seconds)

• Chapter 83：Motivation II- Visualization (5 minutes and 27 seconds)

• Chapter 84：Principal Component Analysis Problem Formulation (9 minutes and 5 seconds)

• Chapter 85：Principal Component Analysis Algorithm (15 minutes and 13 seconds)

• Chapter 86：Choosing the Number of Principal Components (10 minutes and 30 seconds)

• Chapter 87：Reconstruction from Compressed Representation (3 minutes and 54 seconds)

• Chapter 88：Advice for Applying PCA (12 minutes and 48 seconds)

• Chapter 89：Problem Motivation (7 minutes and 38 seconds)

• Chapter 90：Gaussian Distribution (10 minutes and 27 seconds)

• Chapter 91：Algorithm (12 minutes and 2 seconds)

• Chapter 92：Developing and Evaluating an Anomaly Detection System (13 minutes and 7 seconds)

• Chapter 93：Anomaly Detection vs. Supervised Learning (7 minutes and 36 seconds)

• Chapter 94：Choosing What Features to Use (12 minutes and 17 seconds)

• Chapter 95：Multivariate Gaussian Distribution (Optional) (13 minutes and 45 seconds)

• Chapter 96：Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 minutes and 3 seconds)

• Chapter 97：Problem Formulation (7 minutes and 54 seconds)

• Chapter 98：Content Based Recommendations (14 minutes and 31 seconds)

• Chapter 99：Collaborative Filtering (10 minutes and 14 seconds)

• Chapter 100：Collaborative Filtering Algorithm (8 minutes and 26 seconds)

• Chapter 101：Vectorization- Low Rank Matrix Factorization (8 minutes and 27 seconds)

• Chapter 102：Implementational Detail- Mean Normalization (8 minutes and 30 seconds)

• Chapter 103：Learning With Large Datasets (5 minutes and 44 seconds)

• Chapter 104：Stochastic Gradient Descent (13 minutes and 19 seconds)

• Chapter 105：Mini-Batch Gradient Descent (6 minutes and 17 seconds)

• Chapter 106：Stochastic Gradient Descent Convergence (11 minutes and 31 seconds)

• Chapter 107：Online Learning (12 minutes and 50 seconds)

• Chapter 108：Map Reduce and Data Parallelism (14 minutes and 8 seconds)

• Chapter 109：Problem Description and Pipeline (7 minutes and 2 seconds)

• Chapter 110：Sliding Windows (14 minutes and 39 seconds)

• Chapter 111：Getting Lots of Data and Artificial Data (16 minutes and 20 seconds)

• Chapter 112：Ceiling Analysis- What Part of the Pipeline to Work on Next (13 minutes and 50 seconds)

• Chapter 113：Summary and Thank You (4 minutes and 41 seconds)