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In this course we study statistical pattern recognition and machine learning.
The subjects covered are:
General principles of data analysis: overfitting, the bias-variance trade-off, model selection, regularization, the curse of dimensionality.
Linear statistical models for regression and classification.
Clustering and unsupervised learning.
Support vector machines.
Neural networks and deep learning.
Knowledge of elementary probability theory, statistics, multivariable calculus and linear algebra is presupposed.
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