Machine Learning with Coursera
-
Introduction to Machine Learning. Univariate linear regression. (Optional: Linear algebra review.)
-
Multivariate linear regression. Practical aspects of implementation. Octave tutorial.
-
Logistic regression, One-vs-all, Regularization.
-
Neural Networks, backpropagation, gradient checking.
-
Support Vector Machines (SVMs) and intuitions. Quick survey of other algorithms: Naive Bayes, Decision trees, Boosting.
-
Practical advice for applying learning algorithms: How to develop, debugging, feature/model design, setting up experiment structure.
-
Unsupervised learning: Agglomerative clustering, K-means, PCA, when to use each. (Optional/extra credit: ICA).
-
Anomaly detection. Combining supervised and unsupervised.
-
Other applications: Recommender systems. Learning to rank (search).
-
Large-scale/parallel machine learning and big data. ML system design/practical methods. Team design of ML systems.
