Repository Guide | English | 한국어
Last Updated: 2026-10-18
| Unit | Topics | Documents |
|---|---|---|
| Chapter 1 R Programming Basics | R and RStudio, the workspace, basic data types and data frames, variables and assignment, vector creation and operations, basic functions, package installation and visualization | Lecture notes |
| Chapter 2 Introduction to Convergence Informatics | Definitions of informatics and convergence informatics, data science and data scientists, population and sample, central tendency and dispersion, structured and unstructured data, random variables, feature extraction and selection, overview of supervised and unsupervised learning, medical informatics and pharmacoinformatics | Lecture notes |
| Chapter 3 Unsupervised Learning | Clustering vs. classification, similarity and dissimilarity, Manhattan and Euclidean distance, distance matrices and Z-score normalization, Pearson and Spearman correlation, correlation-based distance, hierarchical clustering and linkage methods, K-means clustering | Lecture notes |
| Chapter 4 Dimension Reduction and PCA | Curse of dimensionality, feature selection and feature extraction, projection and manifold learning, linear algebra prerequisites, PCA objectives and derivation with Lagrange multipliers, eigenvalues and eigenvectors of the covariance matrix, MDS, t-SNE, UMAP, scree plots and explained variance ratio | Lecture notes |
| Chapter 5 Modeling | Deterministic and probabilistic models, regression models and variable types, the population linear regression model and error assumptions, least squares estimation and OLS formulas, multiple linear regression, correlation vs. regression, a diet data prediction example | Lecture notes |
| Chapter 6 Supervised Learning | Classification and prediction, the general classification process, training and test sets, overfitting and underfitting, LOOCV and K-fold cross-validation, bias-variance trade-off, model performance metrics, K-nearest neighbors | Lecture notes |