Techniques like Principal Component Analysis (PCA) use linear algebra to strip away useless noise from massive datasets, leaving only the most important features without losing critical data. Part 2: Optimization & Its Applications
The fundamental connecting bridge between both fields. It uses linear algebra to structure a problem and optimization to find the line of best fit for your scattered data points.
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If linear algebra represents the data, optimization is the engine that learns from it. Machine learning usually boils down to minimizing an "error" or "loss" function. Machine learning usually boils down to minimizing an
Linear algebra is the language used to represent and manipulate massive datasets. Instead of looking at individual numbers, we look at entire grids of data at once.
Many real-world problems require finding the best solution subject to strict rules or limitations (e.g., maximizing accuracy while keeping computation costs low). If you need a completely free academic resource
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