Singular Value Decomposition (SVD) and matrix factorization allow us to break a complex matrix down into simpler, foundational parts. This is actively used in recommender systems (like how Netflix predicts what you want to watch).
Machine learning isn't just about writing code; it is deeply rooted in mathematics. Charu Aggarwal’s textbook breaks the curriculum down into two distinct pillars that work in tandem to power modern AI. Part 1: Linear Algebra & Its Applications
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 Charu Aggarwal’s textbook breaks the curriculum down into
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The ultimate workhorse of AI. It is an iterative optimization algorithm used to find the minimum of a function. Imagine being blindfolded on a foggy mountain and finding the bottom by simply stepping in the direction where the ground slopes down the sharpest. It is an iterative optimization algorithm used to
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.
Many real-world problems require finding the best solution subject to strict rules or limitations (e.g., maximizing accuracy while keeping computation costs low). or compress data to find patterns.
This involves moving data from one geometric space to another. It helps us understand how algorithms stretch, rotate, or compress data to find patterns.
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