Dimensionality Reduction Independent Study Syllabus

Brandon Rozek

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Software Developer, Researcher, and Linux Enthusiast.

Dimensionality reduction is the process of reducing the number of random variables under consideration. This study will last for 10 weeks, meeting twice a week for about an hour.

Introduction to Dimensionality Reduction (0.5 Week)

Feature Selection (3 Weeks)

This is the process of selecting a subset of relevant features. The central premise of this technique is that many features are either redundant or irrelevant and thus can be removed without incurring much loss of information.

Metaheuristic Methods (1.5 Weeks)

Optimality Criteria (0.5 Weeks)

Other Feature Selection Techniques (1 Week)

Applications of Metaheuristic Techniques (0.5 Weeks)

Feature Extraction (6 Weeks)

Feature extraction transforms the data in high-dimensional space to a space of fewer dimensions. In other words, feature extraction involves reducing the amount of resources required to describe a large set of data.

Linear Dimensionality Reduction (3 Weeks)

Non-Linear Dimensionality Reduction (3 Weeks)

One approach to the simplification is to assume that the data of interest lie on an embedded non-linear manifold within higher-dimensional space.