The course will introduce the core concepts of machine learning, with an emphasis on the computational, statistical and mathematical foundations of the field. We will study models for both supervised learning and unsupervised learning, introducing these models alongside foundational machine learning concepts, such as maximum likelihood estimation, regularization, information theory, and gradient-based optimization.
| Linear Classification and Regression |
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| Maximum Likelihood |
| Gradient Descent Methods |
| Regularization |
| Neural Networks and Deep Learning |
| Exemplar Based Methods |
| Decision Trees, Bagging and Random Forests |
| Clustering |
| Dimensionality Reduction |