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)
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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