1. Introduction
2. Linear regression with one variable
3. (Optional) Linear algebra review
4. Linear regression with multiple variables
5. Octave tutorial
6. Logistic Regression
7. One-vs-all Classification
8. Regularization
9. Neural Networks
10. Backpropagation Algorithm
11. Practical advise for applying learning algorithms
12. How to develop and debug learning algorithms
13. Feature and model design, setting up experiments
14. Support Vector Machines (SVMs)
15. Survey of other algorithms: Naive Bayes, Decision Trees, Boosting
16. Unsupervised learning: Agglomerative clustering, k-Means, PCA
17. Combining unsupervised and supervised learning.
18. (Optional) Independent component analysis
19. Anomaly detection
20. Other applications: Recommender systems. Learning to rank
21. Large-scale/parallel machine learning and big data.
22. Machine learning design / practical methods
23. Team design of machine learning systems