Standard syllabus
Machine learning intro · Undergraduate · CS / Programming
Topics
Foundations
- Learning problems: classification, regression, clustering
- Train/validation/test splits and cross-validation
- Bias–variance tradeoff and model selection
- Linear and logistic regression
- k-nearest neighbors and naive Bayes
Core methods
- Decision trees and ensemble methods (random forests, boosting intro)
- Support vector machines (intro)
- Neural networks: perceptron, MLP, backprop (intro)
- Clustering: k-means, hierarchical (intro)
- Dimensionality reduction: PCA (intro)
Evaluation and responsibility
- Train/validation/test splits and leakage pitfalls
- Precision, recall, F1, ROC/AUC tradeoffs
- Bias/variance intuition and regularization
- Feature engineering vs representation learning (survey)
- Ethics: fairness, privacy, and dual-use awareness
- Reproducibility: seeds, versions, and data cards (intro)
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$1,162 · Machine learning intro · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.