Machine Learning

Foundations of machine learning: how to go from raw data to a trained, evaluated model.

Prerequisites: basic Python and the Mathematics course (linear algebra, probability, statistics).

Syllabus

1. Introduction
2. General concepts
3. Model evaluation and validation
4. Probabilistic formulation
5. Linear regression
6. Linear classification
7. Multilayer neural networks
8. Regularization and high-dimensional inference
9. Support Vector Machines
10. Decision trees and ensemble methods

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