Data Mining & Machine Learning with R

Introduction to Data mining and Machine Learning

• Statistical learning vs. Machine learning
• Iteration and evaluation
• Bias-Variance trade-off

• Linear regression
• Generalizations and Nonlinearity
• Exercises

• Bayesian refresher
• Naive Bayes
• Dicriminant analysis
• Logistic regression
• K-Nearest neighbors
• Support Vector Machines
• Neural networks
• Decision trees
• Exercises

• Cross-validation approaches
• Bootstrap
• Exercises

• K-means clustering
• Examples
• Challenges of unsupervised learning and beyond K-means

• Ensemble models
• Mixed models
• Boosting
• Examples

• Factor Analysis
• Principal Component Analysis
• Examples