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Classic Algorithmsfirst principles.

MACHINE LEARNING • CONCEPTS FIRST.

Chapter-by-chapter theory notes, step-by-step textbook exercises, video lectures, and SLO-aligned board examination prep for Classic Algorithms (Machine Learning).

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ML Foundations (17)Supervised Learning (21)Classic Algorithms (18)Unsupervised Learning (14)Advanced & Production ML (14)
Syllabus

Curriculum Syllabus

Course chapter sequence roadmap and learning outcomes.

Classic Algorithms (Machine Learning)

1. Support Vector Machines

1.1 Linear SVM Classification
1.2 Soft-Margin Classification
1.3 Nonlinear SVMs & Kernels
1.4 SVM Regression
1.5 Under the Hood

2. Decision Trees

2.1 Training & Visualizing a Tree
2.2 Predictions, Gini & Entropy
2.3 The CART Algorithm
2.4 Regularizing Trees
2.5 Regression Trees & Instability

3. Ensemble Learning

3.1 Voting Classifiers
3.2 Bagging & Pasting
3.3 Random Forests
3.4 AdaBoost
3.5 Gradient Boosting
3.6 Stacking

4. Instance Based and Probabilistic Models

4.1 k-Nearest Neighbors
4.2 Naive Bayes
INTERACTIVE LAB • 18 STEM WORKSTATIONS

Bridge High-School Science to Computational AI

Connect textbook physics, mathematics, and computing to real-world artificial intelligence models and symbolic solvers.

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