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 I. Introduction (Week 1)

 II. Linear Regression with One Variable (Week 1)

 III. Linear Algebra Review (Week 1, Optional)

 IV. Linear Regression with Multiple Variables (Week 2)

 V. Octave Tutorial (Week 2)

 VI. Logistic Regression (Week 3)

 VII. Regularization (Week 3)

 VIII. Neural Networks: Representation (Week 4)

 IX. Neural Networks: Learning (Week 5)

 X. Advice for Applying Machine Learning (Week 6)

 XI. Machine Learning System Design (Week 6)

 XII. Support Vector Machines (Week 7)

 XIII. Clustering (Week 8)

 XIV. Dimensionality Reduction (Week 8)

 XV. Anomaly Detection (Week 9)

 XVI. Recommender Systems (Week 9)

 XVII. Large Scale Machine Learning (Week 10)

 XVIII. Application Example: Photo OCR

 XIX. Conclusion

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Diagnosing Bias vs. Variance (8 min)
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