Module
Undergraduate Machine Learning Foundations
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About
A focused path covering supervised learning fundamentals for UCLA-style CS undergrads (post CS 32/33/35L + discrete math). Assumes calculus and linear algebra; builds from data splits and linear models through regularization to a first neural nets introduction. Emphasizes practice with train/val/test methodology, bias-variance intuition, and implementable models.
Goal
Independently train, validate, and evaluate supervised models (linear/logistic regression and simple neural nets); diagnose overfitting; apply regularization and proper data splitting; explain core concepts mathematically and empirically.
Prerequisites
Tutor
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