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Alejandro's Lectures

Machine Learning Math

Linear Regression and The Exploding Gradients
Even simple models can produce huge problems
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Linear regression in multiple variables
(finally) entering higher dimensions
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The mathematics of optimization for deep learning
A brief guide to minimize a function with millions of variables
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Building an interface for machine learning
Laying the foundations of our handmade machine learning library
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5
Logistic regression
(is a classification algorithm)
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The Maximum Likelihood Estimation
(is the foundation of machine learning)
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Where does the mean squared error come from?
Understanding the most important loss function from a statistical perspective
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3
Linear regression and the least squares problem
Optimizing linear regression by hand
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Linear regression (is the most important algorithm ever)
Dissecting the "Hello, World!" of machine learning
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The taxonomy of machine learning paradigms
Setting the stage for learning problems
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3
Why are neural networks so powerful?
The Universal Approximation Theorem
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Machine learning is not just statistics
Discussing the differences, the similarities, and the nuances
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2
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