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Introduction to Applied Machine Learning
- Statistical learning vs. Machine learning
- Iteration and evaluation
- Bias-Variance trade-off
- Linear regression
- Generalizations and Nonlinearity
- Bayesian refresher
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
Cross-validation and Resampling
- Cross-validation approaches
- K-means clustering
- Challenges of unsupervised learning and beyond K-means
Knowledge of R programming language. Basic familiarity with statistics and linear algebra is recommended.