- 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 classification, Regularization.
- Neural Networks : Representation and Learning
- Practical advice for applying learning algorithms:
- How to develop, debugging, feature/model design, setting up experiment structure.
- Support Vector Machines (SVMs) and the intuition behind them.
- Unsupervised learning
- clustering and dimensionality reduction.
- Anomaly detection.
- Recommender systems.
- Large-scale machine learning.
- An example of an application of machine learning.