Introduction to Machine Learning
ML algorithms are not neutral mathematical facts — they are choices about what to optimize, whose data counts, who bears the error. We derive every algorithm before using it, implement from scratch before touching any library.
Introduction to Machine Learning
ML algorithms are not neutral mathematical facts — they are choices about what to optimize, whose data counts, who bears the error. We derive every algorithm before using it, implement from scratch before touching any library.
Derive every major ML algorithm from first principles
Implement from scratch before libraries
Understand loss, gradient descent, bias-variance, MLE as unifying themes
Evaluate with precision, recall, AUC, calibration, fairness metrics
Conduct structured bias audits
Communicate uncertainty
Build a portfolio of deep engagement
Root Before Branch
Choose Your Depth
Uncertainty Is the Lesson
No Portfolio, No Grade