Brief Introduction
This course focuses on core algorithmic and statistical concepts in machine learning. Topics include pattern recognition, PAC learning, overfitting, decision trees, classification, linear regression, logistic regression, gradient descent, feature projection, dimensionality reduction, maximum likelihood, Bayesian methods, and neural networks.
Description
Tools from machine learning are now ubiquitous in the sciences with applications in engineering, computer vision, and biology, among others. This class introduces the fundamental mathematical models, algorithms, and statistical tools needed to perform core tasks in machine learning. Applications of these ideas are illustrated using programming examples on various data sets.
Topics include pattern recognition, PAC learning, overfitting, decision trees, classification, linear regression, logistic regression, gradient descent, feature projection, dimensionality reduction, maximum likelihood, Bayesian methods, and neural networks.
Knowledge
- â—‹ Techniques for supervised learning including classification and regression.
- â—‹ Algorithms for unsupervised learning including feature extraction.
- â—‹ Statistical methods for interpreting models generated by learning algorithms.
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