2012

19:53 hours

I. Introduction (Week 1)

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Completed Welcome (7 min)

Completed What is Machine Learning? (7 min)

Completed Supervised Learning (12 min)

Completed Unsupervised Learning (14 min)

II. Linear Regression with One Variable (Week 1)

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Completed Model Representation (8 min)

Completed Cost Function (8 min)

Completed Cost Function – Intuition I (11 min)

Completed Cost Function – Intuition II (9 min)

Completed Gradient Descent (11 min)

Completed Gradient Descent Intuition (12 min)

Completed Gradient Descent For Linear Regression (10 min)

Completed What’s Next (6 min)

III. Linear Algebra Review (Week 1, Optional)

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Completed Matrices and Vectors (9 min)

Completed Addition and Scalar Multiplication (7 min)

Completed Matrix Vector Multiplication (14 min)

Completed Matrix Matrix Multiplication (11 min)

Completed Matrix Multiplication Properties (9 min)

Completed Inverse and Transpose (11 min)

IV. Linear Regression with Multiple Variables (Week 2)

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Completed Multiple Features (8 min)

Completed Gradient Descent for Multiple Variables (5 min)

Completed Gradient Descent in Practice I – Feature Scaling (9 min)

Completed Gradient Descent in Practice II – Learning Rate (9 min)

Completed Features and Polynomial Regression (8 min)

Completed Normal Equation (16 min)

Completed Normal Equation Noninvertibility (Optional) (6 min)

V. Octave Tutorial (Week 2)

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Completed Basic Operations (14 min)

Completed Moving Data Around (16 min)

Completed Computing on Data (13 min)

Completed Plotting Data (10 min)

Completed Control Statements: for, while, if statements (13 min)

Completed Vectorization (14 min)

Completed Working on and Submitting Programming Exercises (4 min)

VI. Logistic Regression (Week 3)

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Completed Classification (8 min)

Completed Hypothesis Representation (7 min)

Completed Decision Boundary (15 min)

Completed Cost Function (11 min)

Completed Simplified Cost Function and Gradient Descent (10 min)

Completed Advanced Optimization (14 min)

Completed Multiclass Classification: One-vs-all (6 min)

VII. Regularization (Week 3)

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Completed The Problem of Overfitting (10 min)

Completed Cost Function (10 min)

Completed Regularized Linear Regression (11 min)

Completed Regularized Logistic Regression (9 min)

VIII. Neural Networks: Representation (Week 4)

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Completed Non-linear Hypotheses (10 min)

Completed Neurons and the Brain (8 min)

Completed Model Representation I (12 min)

Completed Model Representation II (12 min)

Completed Examples and Intuitions I (7 min)

Completed Examples and Intuitions II (10 min)

Completed Multiclass Classification (4 min)

IX. Neural Networks: Learning (Week 5)

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Completed Cost Function (7 min)

Completed Backpropagation Algorithm (12 min)

Completed Backpropagation Intuition (13 min)

Completed Implementation Note: Unrolling Parameters (8 min)

Completed Gradient Checking (12 min)

Completed Random Initialization (7 min)

Completed Putting It Together (14 min)

Completed Autonomous Driving (7 min)

X. Advice for Applying Machine Learning (Week 6)

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Completed Deciding What to Try Next (6 min)

Completed Evaluating a Hypothesis (8 min)

Completed Model Selection and Train/Validation/Test Sets (12 min)

Completed Diagnosing Bias vs. Variance (8 min)

Completed Regularization and Bias/Variance (11 min)

Completed Learning Curves (12 min)

Completed Deciding What to Do Next Revisited (7 min)

XI. Machine Learning System Design (Week 6)

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Completed Prioritizing What to Work On (10 min)

Completed Error Analysis (13 min)

Completed Error Metrics for Skewed Classes (12 min)

Completed Trading Off Precision and Recall (14 min)

Completed Data For Machine Learning (11 min)

XII. Support Vector Machines (Week 7)

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Completed Optimization Objective (15 min)

Completed Large Margin Intuition (11 min)

Completed Mathematics Behind Large Margin Classification (Optional) (20 min)

Completed Kernels I (16 min)

Completed Kernels II (16 min)

Completed Using An SVM (21 min)

XIII. Clustering (Week 8)

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Completed Unsupervised Learning: Introduction (3 min)

Completed K-Means Algorithm (13 min)

Completed Optimization Objective (7 min)

Completed Random Initialization (8 min)

Completed Choosing the Number of Clusters (8 min)

XIV. Dimensionality Reduction (Week 8)

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Completed Motivation I: Data Compression (10 min)

Completed Motivation II: Visualization (6 min)

Completed Principal Component Analysis Problem Formulation (9 min)

Completed Principal Component Analysis Algorithm (15 min)

Completed Choosing the Number of Principal Components (11 min)

Completed Reconstruction from Compressed Representation (4 min)

Completed Advice for Applying PCA (13 min)

XV. Anomaly Detection (Week 9)

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Completed Problem Motivation (8 min)

Completed Gaussian Distribution (10 min)

Completed Algorithm (12 min)

Completed Developing and Evaluating an Anomaly Detection System (13 min)

Completed Anomaly Detection vs. Supervised Learning (8 min)

Completed Choosing What Features to Use (12 min)

Completed Multivariate Gaussian Distribution (Optional) (14 min)

Completed Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min)

XVI. Recommender Systems (Week 9)

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Completed Problem Formulation (8 min)

Completed Content Based Recommendations (15 min)

Completed Collaborative Filtering (10 min)

Completed Collaborative Filtering Algorithm (9 min)

Completed Vectorization: Low Rank Matrix Factorization (8 min)

Completed Implementational Detail: Mean Normalization (9 min)

XVII. Large Scale Machine Learning (Week 10)

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Completed Learning With Large Datasets (6 min)

Completed Stochastic Gradient Descent (13 min)

Completed Mini-Batch Gradient Descent (6 min)

Completed Stochastic Gradient Descent Convergence (12 min)

Completed Online Learning (13 min)

Completed Map Reduce and Data Parallelism (14 min)

XVIII. Application Example: Photo OCR

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Completed Problem Description and Pipeline (7 m