Machine Learning isn’t just magic—it’s math! 🔍 In this tech talk, we dive into the statistical and algebraic foundations that power modern machine learning algorithms.
We'll explore how statistics helps us model uncertainty, infer patterns from data, and evaluate predictions, while linear algebra gives us the tools to manipulate and transform data efficiently-key to understanding models like linear regression, neural networks, and PCA.
Whether you're training a neural net, tuning a recommender system, or just curious about what’s under the hood of ML frameworks-this talk is your gateway to the mathematical muscle behind machine learning.
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Statistical concepts: distributions, estimators, hypothesis testing
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Algebraic tools: vectors, matrices, eigenvalues
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Real-world applications: from regression to deep learning
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Tips on how to strengthen your ML math skills
Ideal for developers, data enthusiasts, and anyone interested in making their ML knowledge more mathematically grounded.
Statistical and Algebraic Method in Machine Learning
