These notes are a premium resource, combining lecture materials, lab exercises, and assignment insights into one detailed, easy-to-follow document. They are provided as a knitted word file created from an R Markdown document. What’s included: - Key theories and formulas: Simplified explanations of essential concepts to enhance understanding. - Step-by-step R codes: Practical examples and outputs to apply theory to real-world problems. - Well-organised topics: Notes are structured week-by-week, following the course content closely. - Helpful functions: A curated list of functions to simplify statistical computations. Topics covered: Week 1: Basic Concepts in Statistical Learning Week 2: Multivariate Data, Random Sampling Week 3: Multivariate Normal Distribution Week 4: Inference for Multivariate Data Week 5: Principal Component Analysis (PCA) Week 6: Factor Analysis Week 7: Canonical Correlation Analysis Week 8: Clustering Analysis Week 9: Linear Discriminant Analysis Week 10: Logistic Regression Week 11: Principal Component Regression