- Introduction to Machine Learning. Univariate linear regression.
- Multivariate linear regression. Practical aspects of implementation.
- Logistic regression, One-vs-all classification, Regularization.
- Neural Networks.
- 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.
paulepps/Machine-Learning
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