Machine Learning with Coursera

1. Introduction to Machine Learning. Univariate linear regression. (Optional: Linear algebra review.)

2. Multivariate linear regression. Practical aspects of implementation. Octave tutorial.

3. Logistic regression, One-vs-all, Regularization.

4. Neural Networks, backpropagation, gradient checking.

5. Support Vector Machines (SVMs) and intuitions. Quick survey of other algorithms: Naive Bayes, Decision trees, Boosting.

6. Practical advice for applying learning algorithms: How to develop, debugging, feature/model design, setting up experiment structure.

7. Unsupervised learning: Agglomerative clustering, K-means, PCA, when to use each. (Optional/extra credit: ICA).

8. Anomaly detection. Combining supervised and unsupervised.

9. Other applications: Recommender systems. Learning to rank (search).

10. Large-scale/parallel machine learning and big data. ML system design/practical methods. Team design of ML systems.